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/*
* Licensed to the Apache Software Foundation (ASF) under one
* or more contributor license agreements. See the NOTICE file
* distributed with this work for additional information
* regarding copyright ownership. The ASF licenses this file
* to you under the Apache License, Version 2.0 (the
* "License"); you may not use this file except in compliance
* with the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
/*-------------------------------------------------------------------------
*
* selfuncs.c
* Selectivity functions and index cost estimation functions for
* standard operators and index access methods.
*
* Selectivity routines are registered in the pg_operator catalog
* in the "oprrest" and "oprjoin" attributes.
*
* Index cost functions are registered in the pg_am catalog
* in the "amcostestimate" attribute.
*
* Portions Copyright (c) 2006-2009, Greenplum inc
* Portions Copyright (c) 1996-2009, PostgreSQL Global Development Group
* Portions Copyright (c) 1994, Regents of the University of California
*
*
* IDENTIFICATION
* $PostgreSQL: pgsql/src/backend/utils/adt/selfuncs.c,v 1.214.2.6 2007/08/31 23:35:29 tgl Exp $
*
*-------------------------------------------------------------------------
*/
/*----------
* Operator selectivity estimation functions are called to estimate the
* selectivity of WHERE clauses whose top-level operator is their operator.
* We divide the problem into two cases:
* Restriction clause estimation: the clause involves vars of just
* one relation.
* Join clause estimation: the clause involves vars of multiple rels.
* Join selectivity estimation is far more difficult and usually less accurate
* than restriction estimation.
*
* When dealing with the inner scan of a nestloop join, we consider the
* join's joinclauses as restriction clauses for the inner relation, and
* treat vars of the outer relation as parameters (a/k/a constants of unknown
* values). So, restriction estimators need to be able to accept an argument
* telling which relation is to be treated as the variable.
*
* The call convention for a restriction estimator (oprrest function) is
*
* Selectivity oprrest (PlannerInfo *root,
* Oid operator,
* List *args,
* int varRelid);
*
* root: general information about the query (rtable and RelOptInfo lists
* are particularly important for the estimator).
* operator: OID of the specific operator in question.
* args: argument list from the operator clause.
* varRelid: if not zero, the relid (rtable index) of the relation to
* be treated as the variable relation. May be zero if the args list
* is known to contain vars of only one relation.
*
* This is represented at the SQL level (in pg_proc) as
*
* float8 oprrest (internal, oid, internal, int4);
*
* The call convention for a join estimator (oprjoin function) is similar
* except that varRelid is not needed, and instead the join type is
* supplied:
*
* Selectivity oprjoin (PlannerInfo *root,
* Oid operator,
* List *args,
* JoinType jointype);
*
* float8 oprjoin (internal, oid, internal, int2);
*
* (We deliberately make the SQL signature different to facilitate
* catching errors.)
*----------
*/
#include "postgres.h"
#include <ctype.h>
#include <math.h>
#include "catalog/catquery.h"
#include "catalog/pg_opclass.h"
#include "catalog/pg_statistic.h"
#include "catalog/pg_type.h"
#include "catalog/pg_constraint.h"
#include "mb/pg_wchar.h"
#include "nodes/makefuncs.h"
#include "optimizer/clauses.h"
#include "optimizer/cost.h"
#include "optimizer/pathnode.h"
#include "optimizer/paths.h"
#include "optimizer/plancat.h"
#include "optimizer/restrictinfo.h"
#include "optimizer/var.h"
#include "parser/parse_coerce.h"
#include "parser/parse_expr.h"
#include "parser/parsetree.h"
#include "utils/builtins.h"
#include "utils/date.h"
#include "utils/datum.h"
#include "utils/fmgroids.h"
#include "utils/lsyscache.h"
#include "utils/nabstime.h"
#include "utils/pg_locale.h"
#include "utils/selfuncs.h"
#include "utils/syscache.h"
#include "cdb/cdbvars.h" /* getgpsegmentCount */
static double ineq_histogram_selectivity(VariableStatData *vardata,
FmgrInfo *opproc, bool isgt,
Datum constval, Oid consttype);
static bool convert_to_scalar(Datum value, Oid valuetypid, double *scaledvalue,
Datum lobound, Datum hibound, Oid boundstypid,
double *scaledlobound, double *scaledhibound, bool isgt);
static double convert_numeric_to_scalar(Datum value, Oid typid);
static void convert_bytea_to_scalar(Datum value,
double *scaledvalue,
Datum lobound,
double *scaledlobound,
Datum hibound,
double *scaledhibound);
static double convert_one_bytea_to_scalar(unsigned char *value, int valuelen,
int rangelo, int rangehi);
static bool get_variable_maximum(PlannerInfo *root, VariableStatData *vardata,
Oid sortop, Datum *max);
static Selectivity prefix_selectivity(VariableStatData *vardata,
Oid opclass, Const *prefixcon);
static Selectivity pattern_selectivity(Const *patt, Pattern_Type ptype);
static Datum string_to_datum(const char *str, Oid datatype);
static Const *string_to_const(const char *str, Oid datatype);
static Const *string_to_bytea_const(const char *str, size_t str_len);
static Selectivity
mcv_selectivity_cdb(VariableStatData *vardata,
FmgrInfo *opproc,
Datum constval,
bool varonleft,
Selectivity *sumcommonp, /* OUT */
double *nvaluesp); /* OUT */
/*
* eqsel - Selectivity of "=" for any data types.
*
* Note: this routine is also used to estimate selectivity for some
* operators that are not "=" but have comparable selectivity behavior,
* such as "~=" (geometric approximate-match). Even for "=", we must
* keep in mind that the left and right datatypes may differ.
*/
Datum
eqsel(PG_FUNCTION_ARGS)
{
PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0);
Oid operator = PG_GETARG_OID(1);
List *args = (List *) PG_GETARG_POINTER(2);
int varRelid = PG_GETARG_INT32(3);
VariableStatData vardata;
Node *other = NULL;
bool varonleft = false;
Datum *values;
int nvalues;
float4 *numbers;
int nnumbers;
double selec;
/*
* If expression is not variable = something or something = variable, then
* punt and return a default estimate.
*/
if (!get_restriction_variable(root, args, varRelid,
&vardata, &other, &varonleft))
PG_RETURN_FLOAT8(DEFAULT_EQ_SEL);
/*
* If the something is a NULL constant, assume operator is strict and
* return zero, ie, operator will never return TRUE.
*/
if (IsA(other, Const) &&
((Const *) other)->constisnull)
{
ReleaseVariableStats(vardata);
PG_RETURN_FLOAT8(0.0);
}
if (HeapTupleIsValid(getStatsTuple(&vardata)))
{
Form_pg_statistic stats;
HeapTuple tp = getStatsTuple(&vardata);
stats = (Form_pg_statistic) GETSTRUCT(tp);
if (IsA(other, Const))
{
/* Variable is being compared to a known non-null constant */
Datum constval = ((Const *) other)->constvalue;
bool match = false;
int i;
/*
* Is the constant "=" to any of the column's most common values?
* (Although the given operator may not really be "=", we will
* assume that seeing whether it returns TRUE is an appropriate
* test. If you don't like this, maybe you shouldn't be using
* eqsel for your operator...)
*/
if (get_attstatsslot(tp,
vardata.atttype, vardata.atttypmod,
STATISTIC_KIND_MCV, InvalidOid,
&values, &nvalues,
&numbers, &nnumbers))
{
FmgrInfo eqproc;
fmgr_info(get_opcode(operator), &eqproc);
for (i = 0; i < nvalues; i++)
{
/* be careful to apply operator right way 'round */
if (varonleft)
match = DatumGetBool(FunctionCall2(&eqproc,
values[i],
constval));
else
match = DatumGetBool(FunctionCall2(&eqproc,
constval,
values[i]));
if (match)
break;
}
}
else
{
/* no most-common-value info available */
values = NULL;
numbers = NULL;
i = nvalues = nnumbers = 0;
}
if (match)
{
/*
* Constant is "=" to this common value. We know selectivity
* exactly (or as exactly as ANALYZE could calculate it,
* anyway).
*/
selec = numbers[i];
}
else
{
/*
* Comparison is against a constant that is neither NULL nor
* any of the common values. Its selectivity cannot be more
* than this:
*/
double sumcommon = 0.0;
double otherdistinct;
for (i = 0; i < nnumbers; i++)
sumcommon += numbers[i];
selec = 1.0 - sumcommon - stats->stanullfrac;
CLAMP_PROBABILITY(selec);
/*
* and in fact it's probably a good deal less. We approximate
* that all the not-common values share this remaining
* fraction equally, so we divide by the number of other
* distinct values.
*/
otherdistinct = get_variable_numdistinct(&vardata)
- nnumbers;
if (otherdistinct > 1)
selec /= otherdistinct;
/*
* Another cross-check: selectivity shouldn't be estimated as
* more than the least common "most common value".
*/
if (nnumbers > 0 && selec > numbers[nnumbers - 1])
selec = numbers[nnumbers - 1];
}
free_attstatsslot(vardata.atttype, values, nvalues,
numbers, nnumbers);
}
else
{
double ndistinct;
/*
* Search is for a value that we do not know a priori, but we will
* assume it is not NULL. Estimate the selectivity as non-null
* fraction divided by number of distinct values, so that we get a
* result averaged over all possible values whether common or
* uncommon. (Essentially, we are assuming that the not-yet-known
* comparison value is equally likely to be any of the possible
* values, regardless of their frequency in the table. Is that a
* good idea?)
*/
selec = 1.0 - stats->stanullfrac;
ndistinct = get_variable_numdistinct(&vardata);
if (ndistinct > 1)
selec /= ndistinct;
/*
* Cross-check: selectivity should never be estimated as more than
* the most common value's.
*/
if (get_attstatsslot(tp,
vardata.atttype, vardata.atttypmod,
STATISTIC_KIND_MCV, InvalidOid,
NULL, NULL,
&numbers, &nnumbers))
{
if (nnumbers > 0 && selec > numbers[0])
selec = numbers[0];
free_attstatsslot(vardata.atttype, NULL, 0, numbers, nnumbers);
}
}
}
else
{
/*
* No ANALYZE stats available, so make a guess using estimated number
* of distinct values and assuming they are equally common. (The guess
* is unlikely to be very good, but we do know a few special cases.)
*/
selec = 1.0 / get_variable_numdistinct(&vardata);
}
ReleaseVariableStats(vardata);
/* result should be in range, but make sure... */
CLAMP_PROBABILITY(selec);
PG_RETURN_FLOAT8((float8) selec);
}
/*
* neqsel - Selectivity of "!=" for any data types.
*
* This routine is also used for some operators that are not "!="
* but have comparable selectivity behavior. See above comments
* for eqsel().
*/
Datum
neqsel(PG_FUNCTION_ARGS)
{
PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0);
Oid operator = PG_GETARG_OID(1);
List *args = (List *) PG_GETARG_POINTER(2);
int varRelid = PG_GETARG_INT32(3);
Oid eqop;
float8 result;
/*
* We want 1 - eqsel() where the equality operator is the one associated
* with this != operator, that is, its negator.
*/
eqop = get_negator(operator);
if (eqop)
{
result = DatumGetFloat8(DirectFunctionCall4(eqsel,
PointerGetDatum(root),
ObjectIdGetDatum(eqop),
PointerGetDatum(args),
Int32GetDatum(varRelid)));
}
else
{
/* Use default selectivity (should we raise an error instead?) */
result = DEFAULT_EQ_SEL;
}
result = 1.0 - result;
PG_RETURN_FLOAT8(result);
}
/*
* scalarineqsel - Selectivity of "<", "<=", ">", ">=" for scalars.
*
* This is the guts of both scalarltsel and scalargtsel. The caller has
* commuted the clause, if necessary, so that we can treat the variable as
* being on the left. The caller must also make sure that the other side
* of the clause is a non-null Const, and dissect same into a value and
* datatype.
*
* This routine works for any datatype (or pair of datatypes) known to
* convert_to_scalar(). If it is applied to some other datatype,
* it will return a default estimate.
*/
static double
scalarineqsel(PlannerInfo *root, Oid operator, bool isgt,
VariableStatData *vardata, Datum constval, Oid consttype)
{
Form_pg_statistic stats;
FmgrInfo opproc;
double mcv_selec,
hist_selec,
sumcommon;
double selec;
HeapTuple tp = getStatsTuple(vardata);
if (!HeapTupleIsValid(getStatsTuple(vardata)))
{
/* no stats available, so default result */
return DEFAULT_INEQ_SEL;
}
stats = (Form_pg_statistic) GETSTRUCT(tp);
fmgr_info(get_opcode(operator), &opproc);
/*
* If we have most-common-values info, add up the fractions of the MCV
* entries that satisfy MCV OP CONST. These fractions contribute directly
* to the result selectivity. Also add up the total fraction represented
* by MCV entries.
*/
mcv_selec = mcv_selectivity(vardata, &opproc, constval, true,
&sumcommon);
/*
* If there is a histogram, determine which bin the constant falls in, and
* compute the resulting contribution to selectivity.
*/
hist_selec = ineq_histogram_selectivity(vardata, &opproc, isgt,
constval, consttype);
/*
* Now merge the results from the MCV and histogram calculations,
* realizing that the histogram covers only the non-null values that are
* not listed in MCV.
*/
selec = 1.0 - stats->stanullfrac - sumcommon;
if (hist_selec > 0.0)
selec *= hist_selec;
else
{
/*
* If no histogram but there are values not accounted for by MCV,
* arbitrarily assume half of them will match.
*/
selec *= 0.5;
}
selec += mcv_selec;
/* result should be in range, but make sure... */
CLAMP_PROBABILITY(selec);
return selec;
}
/*
* mcv_selectivity - Examine the MCV list for selectivity estimates
*
* Determine the fraction of the variable's MCV population that satisfies
* the predicate (VAR OP CONST), or (CONST OP VAR) if !varonleft. Also
* compute the fraction of the total column population represented by the MCV
* list. This code will work for any boolean-returning predicate operator.
*
* The function result is the MCV selectivity, and the fraction of the
* total population is returned into *sumcommonp. Zeroes are returned
* if there is no MCV list.
*
* CDB: The number of MCVs is returned into *nvaluesp.
*/
double
mcv_selectivity(VariableStatData *vardata, FmgrInfo *opproc,
Datum constval, bool varonleft,
double *sumcommonp)
{
return mcv_selectivity_cdb(vardata, opproc, constval, varonleft,
sumcommonp, NULL);
}
static Selectivity
mcv_selectivity_cdb(VariableStatData *vardata,
FmgrInfo *opproc,
Datum constval,
bool varonleft,
Selectivity *sumcommonp, /* OUT */
double *nvaluesp) /* OUT */
{
double mcv_selec,
sumcommon;
Datum *values;
int nvalues = 0;
float4 *numbers;
int nnumbers;
int i;
HeapTuple tp = getStatsTuple(vardata);
mcv_selec = 0.0;
sumcommon = 0.0;
if (HeapTupleIsValid(tp) &&
get_attstatsslot(tp,
vardata->atttype, vardata->atttypmod,
STATISTIC_KIND_MCV, InvalidOid,
&values, &nvalues,
&numbers, &nnumbers))
{
for (i = 0; i < nvalues; i++)
{
if (varonleft ?
DatumGetBool(FunctionCall2(opproc,
values[i],
constval)) :
DatumGetBool(FunctionCall2(opproc,
constval,
values[i])))
mcv_selec += numbers[i];
sumcommon += numbers[i];
}
free_attstatsslot(vardata->atttype, values, nvalues,
numbers, nnumbers);
}
*sumcommonp = sumcommon;
if (nvaluesp)
*nvaluesp = nvalues;
return mcv_selec;
}
/*
* histogram_selectivity - Examine the histogram for selectivity estimates
*
* Determine the fraction of the variable's histogram entries that satisfy
* the predicate (VAR OP CONST), or (CONST OP VAR) if !varonleft.
*
* This code will work for any boolean-returning predicate operator, whether
* or not it has anything to do with the histogram sort operator. We are
* essentially using the histogram just as a representative sample. However,
* small histograms are unlikely to be all that representative, so the caller
* should specify a minimum histogram size to use, and fall back on some
* other approach if this routine fails.
*
* The caller also specifies n_skip, which causes us to ignore the first and
* last n_skip histogram elements, on the grounds that they are outliers and
* hence not very representative. If in doubt, min_hist_size = 100 and
* n_skip = 1 are reasonable values.
*
* The function result is the selectivity, or -1 if there is no histogram
* or it's smaller than min_hist_size.
*
* Note that the result disregards both the most-common-values (if any) and
* null entries. The caller is expected to combine this result with
* statistics for those portions of the column population. It may also be
* prudent to clamp the result range, ie, disbelieve exact 0 or 1 outputs.
*/
double
histogram_selectivity(VariableStatData *vardata, FmgrInfo *opproc,
Datum constval, bool varonleft,
int min_hist_size, int n_skip)
{
double result;
Datum *values;
int nvalues;
HeapTuple tp = getStatsTuple(vardata);
/* check sanity of parameters */
Assert(n_skip >= 0);
Assert(min_hist_size > 2 * n_skip);
if (HeapTupleIsValid(tp) &&
get_attstatsslot(tp,
vardata->atttype, vardata->atttypmod,
STATISTIC_KIND_HISTOGRAM, InvalidOid,
&values, &nvalues,
NULL, NULL))
{
if (nvalues >= min_hist_size)
{
int nmatch = 0;
int i;
for (i = n_skip; i < nvalues - n_skip; i++)
{
if (varonleft ?
DatumGetBool(FunctionCall2(opproc,
values[i],
constval)) :
DatumGetBool(FunctionCall2(opproc,
constval,
values[i])))
nmatch++;
}
result = ((double) nmatch) / ((double) (nvalues - 2 * n_skip));
}
else
result = -1;
free_attstatsslot(vardata->atttype, values, nvalues, NULL, 0);
}
else
result = -1;
return result;
}
/*
* ineq_histogram_selectivity - Examine the histogram for scalarineqsel
*
* Determine the fraction of the variable's histogram population that
* satisfies the inequality condition, ie, VAR < CONST or VAR > CONST.
*
* Returns zero if there is no histogram (valid results will always be
* greater than zero).
*
* Note that the result disregards both the most-common-values (if any) and
* null entries. The caller is expected to combine this result with
* statistics for those portions of the column population.
*/
static double
ineq_histogram_selectivity(VariableStatData *vardata,
FmgrInfo *opproc, bool isgt,
Datum constval, Oid consttype)
{
double hist_selec;
Datum *values;
int nvalues;
HeapTuple tp = getStatsTuple(vardata);
hist_selec = 0.0;
/*
* Someday, ANALYZE might store more than one histogram per rel/att,
* corresponding to more than one possible sort ordering defined for the
* column type. However, to make that work we will need to figure out
* which staop to search for --- it's not necessarily the one we have at
* hand! (For example, we might have a '<=' operator rather than the '<'
* operator that will appear in staop.) For now, assume that whatever
* appears in pg_statistic is sorted the same way our operator sorts, or
* the reverse way if isgt is TRUE.
*/
if (HeapTupleIsValid(tp) &&
get_attstatsslot(tp,
vardata->atttype, vardata->atttypmod,
STATISTIC_KIND_HISTOGRAM, InvalidOid,
&values, &nvalues,
NULL, NULL))
{
if (nvalues > 1)
{
/*
* Use binary search to find proper location, ie, the first slot
* at which the comparison fails. (If the given operator isn't
* actually sort-compatible with the histogram, you'll get garbage
* results ... but probably not any more garbage-y than you would
* from the old linear search.)
*/
double histfrac;
int lobound = 0; /* first possible slot to search */
int hibound = nvalues; /* last+1 slot to search */
while (lobound < hibound)
{
int probe = (lobound + hibound) / 2;
bool ltcmp;
ltcmp = DatumGetBool(FunctionCall2(opproc,
values[probe],
constval));
if (isgt)
ltcmp = !ltcmp;
if (ltcmp)
lobound = probe + 1;
else
hibound = probe;
}
if (lobound <= 0)
{
/* Constant is below lower histogram boundary. */
histfrac = 0.0;
}
else if (lobound >= nvalues)
{
/* Constant is above upper histogram boundary. */
histfrac = 1.0;
}
else
{
int i = lobound;
double val,
high,
low;
double binfrac;
/*
* We have values[i-1] < constant < values[i].
*
* Convert the constant and the two nearest bin boundary
* values to a uniform comparison scale, and do a linear
* interpolation within this bin.
*/
if (convert_to_scalar(constval, consttype, &val,
values[i - 1], values[i],
vardata->vartype,
&low, &high, isgt))
{
if (high <= low)
{
/* cope if bin boundaries appear identical */
binfrac = 0.5;
}
else if (val <= low)
binfrac = 0.0;
else if (val >= high)
binfrac = 1.0;
else
{
binfrac = (val - low) / (high - low);
/*
* Watch out for the possibility that we got a NaN or
* Infinity from the division. This can happen
* despite the previous checks, if for example "low"
* is -Infinity.
*/
if (isnan(binfrac) ||
binfrac < 0.0 || binfrac > 1.0)
binfrac = 0.5;
}
}
else
{
/*
* Ideally we'd produce an error here, on the grounds that
* the given operator shouldn't have scalarXXsel
* registered as its selectivity func unless we can deal
* with its operand types. But currently, all manner of
* stuff is invoking scalarXXsel, so give a default
* estimate until that can be fixed.
*/
binfrac = 0.5;
}
/*
* Now, compute the overall selectivity across the values
* represented by the histogram. We have i-1 full bins and
* binfrac partial bin below the constant.
*/
histfrac = (double) (i - 1) + binfrac;
histfrac /= (double) (nvalues - 1);
}
/*
* Now histfrac = fraction of histogram entries below the
* constant.
*
* Account for "<" vs ">"
*/
hist_selec = isgt ? (1.0 - histfrac) : histfrac;
/*
* The histogram boundaries are only approximate to begin with,
* and may well be out of date anyway. Therefore, don't believe
* extremely small or large selectivity estimates.
*/
if (hist_selec < 0.0001)
hist_selec = 0.0001;
else if (hist_selec > 0.9999)
hist_selec = 0.9999;
}
free_attstatsslot(vardata->atttype, values, nvalues, NULL, 0);
}
return hist_selec;
}
/*
* scalarltsel - Selectivity of "<" (also "<=") for scalars.
*/
Datum
scalarltsel(PG_FUNCTION_ARGS)
{
PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0);
Oid operator = PG_GETARG_OID(1);
List *args = (List *) PG_GETARG_POINTER(2);
int varRelid = PG_GETARG_INT32(3);
VariableStatData vardata;
Node *other = NULL;
bool varonleft = false;
Datum constval;
Oid consttype;
bool isgt;
double selec;
/*
* If expression is not variable op something or something op variable,
* then punt and return a default estimate.
*/
if (!get_restriction_variable(root, args, varRelid,
&vardata, &other, &varonleft))
PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
/*
* Can't do anything useful if the something is not a constant, either.
*/
if (!IsA(other, Const))
{
ReleaseVariableStats(vardata);
PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
}
/*
* If the constant is NULL, assume operator is strict and return zero, ie,
* operator will never return TRUE.
*/
if (((Const *) other)->constisnull)
{
ReleaseVariableStats(vardata);
PG_RETURN_FLOAT8(0.0);
}
constval = ((Const *) other)->constvalue;
consttype = ((Const *) other)->consttype;
/*
* Force the var to be on the left to simplify logic in scalarineqsel.
*/
if (varonleft)
{
/* we have var < other */
isgt = false;
}
else
{
/* we have other < var, commute to make var > other */
operator = get_commutator(operator);
if (!operator)
{
/* Use default selectivity (should we raise an error instead?) */
ReleaseVariableStats(vardata);
PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
}
isgt = true;
}
selec = scalarineqsel(root, operator, isgt, &vardata, constval, consttype);
ReleaseVariableStats(vardata);
PG_RETURN_FLOAT8((float8) selec);
}
/*
* scalargtsel - Selectivity of ">" (also ">=") for integers.
*/
Datum
scalargtsel(PG_FUNCTION_ARGS)
{
PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0);
Oid operator = PG_GETARG_OID(1);
List *args = (List *) PG_GETARG_POINTER(2);
int varRelid = PG_GETARG_INT32(3);
VariableStatData vardata;
Node *other = NULL;
bool varonleft = false;
Datum constval;
Oid consttype;
bool isgt;
double selec;
/*
* If expression is not variable op something or something op variable,
* then punt and return a default estimate.
*/
if (!get_restriction_variable(root, args, varRelid,
&vardata, &other, &varonleft))
PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
/*
* Can't do anything useful if the something is not a constant, either.
*/
if (!IsA(other, Const))
{
ReleaseVariableStats(vardata);
PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
}
/*
* If the constant is NULL, assume operator is strict and return zero, ie,
* operator will never return TRUE.
*/
if (((Const *) other)->constisnull)
{
ReleaseVariableStats(vardata);
PG_RETURN_FLOAT8(0.0);
}
constval = ((Const *) other)->constvalue;
consttype = ((Const *) other)->consttype;
/*
* Force the var to be on the left to simplify logic in scalarineqsel.
*/
if (varonleft)
{
/* we have var > other */
isgt = true;
}
else
{
/* we have other > var, commute to make var < other */
operator = get_commutator(operator);
if (!operator)
{
/* Use default selectivity (should we raise an error instead?) */
ReleaseVariableStats(vardata);
PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
}
isgt = false;
}
selec = scalarineqsel(root, operator, isgt, &vardata, constval, consttype);
ReleaseVariableStats(vardata);
PG_RETURN_FLOAT8((float8) selec);
}
/*
* patternsel - Generic code for pattern-match selectivity.
*/
static double
patternsel(PG_FUNCTION_ARGS, Pattern_Type ptype, bool negate)
{
PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0);
Oid operator = PG_GETARG_OID(1);
List *args = (List *) PG_GETARG_POINTER(2);
int varRelid = PG_GETARG_INT32(3);
VariableStatData vardata;
Node *variable;
Node *other=NULL;
bool varonleft=false;
Datum constval;
Oid consttype;
Oid vartype;
Oid opclass;
Pattern_Prefix_Status pstatus;
Const *patt = NULL;
Const *prefix = NULL;
Const *rest = NULL;
double result;
/*
* If this is for a NOT LIKE or similar operator, get the corresponding
* positive-match operator and work with that. Set result to the
* correct default estimate, too.
*/
if (negate)
{
operator = get_negator(operator);
if (!OidIsValid(operator))
elog(ERROR, "patternsel called for operator without a negator");
result = 1.0 - DEFAULT_MATCH_SEL;
}
else
{
result = DEFAULT_MATCH_SEL;
}
/*
* If expression is not variable op constant, then punt and return a
* default estimate.
*/
if (!get_restriction_variable(root, args, varRelid,
&vardata, &other, &varonleft))
return result;
if (!varonleft || !IsA(other, Const))
{
ReleaseVariableStats(vardata);
return result;
}
variable = (Node *) linitial(args);
/*
* If the constant is NULL, assume operator is strict and return zero, ie,
* operator will never return TRUE. (It's zero even for a negator op.)
*/
if (((Const *) other)->constisnull)
{
ReleaseVariableStats(vardata);
return 0.0;
}
constval = ((Const *) other)->constvalue;
consttype = ((Const *) other)->consttype;
/*
* The right-hand const is type text or bytea for all supported operators.
* We do not expect to see binary-compatible types here, since
* const-folding should have relabeled the const to exactly match the
* operator's declared type.
*/
if (consttype != TEXTOID && consttype != BYTEAOID)
{
ReleaseVariableStats(vardata);
return result;
}
/*
* Similarly, the exposed type of the left-hand side should be one of
* those we know. (Do not look at vardata.atttype, which might be
* something binary-compatible but different.) We can use it to choose
* the index opclass from which we must draw the comparison operators.
*
* NOTE: It would be more correct to use the PATTERN opclasses than the
* simple ones, but at the moment ANALYZE will not generate statistics for
* the PATTERN operators. But our results are so approximate anyway that
* it probably hardly matters.
*/
vartype = vardata.vartype;
switch (vartype)
{
case TEXTOID:
opclass = TEXT_BTREE_OPS_OID;
break;
case VARCHAROID:
opclass = VARCHAR_BTREE_OPS_OID;
break;
case BPCHAROID:
opclass = BPCHAR_BTREE_OPS_OID;
break;
case NAMEOID:
opclass = NAME_BTREE_OPS_OID;
break;
case BYTEAOID:
opclass = BYTEA_BTREE_OPS_OID;
break;
default:
ReleaseVariableStats(vardata);
return result;
}
/* divide pattern into fixed prefix and remainder */
patt = (Const *) other;
pstatus = pattern_fixed_prefix(patt, ptype, &prefix, &rest);
/*
* If necessary, coerce the prefix constant to the right type. (The "rest"
* constant need not be changed.)
*/
if (prefix && prefix->consttype != vartype)
{
char *prefixstr;
switch (prefix->consttype)
{
case TEXTOID:
prefixstr = DatumGetCString(DirectFunctionCall1(textout,
prefix->constvalue));
break;
case BYTEAOID:
prefixstr = DatumGetCString(DirectFunctionCall1(byteaout,
prefix->constvalue));
break;
default:
elog(ERROR, "unrecognized consttype: %u",
prefix->consttype);
ReleaseVariableStats(vardata);
return result;
}
prefix = string_to_const(prefixstr, vartype);
pfree(prefixstr);
}
if (pstatus == Pattern_Prefix_Exact)
{
/*
* Pattern specifies an exact match, so pretend operator is '='
*/
Oid eqopr = get_opclass_member(opclass, InvalidOid,
BTEqualStrategyNumber);
List *eqargs;
if (eqopr == InvalidOid)
elog(ERROR, "no = operator for opclass %u", opclass);
eqargs = list_make2(variable, prefix);
result = DatumGetFloat8(DirectFunctionCall4(eqsel,
PointerGetDatum(root),
ObjectIdGetDatum(eqopr),
PointerGetDatum(eqargs),
Int32GetDatum(varRelid)));
}
else
{
/*
* Not exact-match pattern. If we have a sufficiently large
* histogram, estimate selectivity for the histogram part of the
* population by counting matches in the histogram. If not, estimate
* selectivity of the fixed prefix and remainder of pattern
* separately, then combine the two to get an estimate of the
* selectivity for the part of the column population represented by
* the histogram. We then add up data for any most-common-values
* values; these are not in the histogram population, and we can get
* exact answers for them by applying the pattern operator, so there's
* no reason to approximate. (If the MCVs cover a significant part of
* the total population, this gives us a big leg up in accuracy.)
*/
Selectivity selec;
Selectivity eqsel;
Selectivity fewsel;
double fewvalues = 2.0;
double ncommon;
double ndistinct;
FmgrInfo opproc;
double nullfrac,
mcv_selec,
sumcommon;
/* Try to use the histogram entries to get selectivity */
fmgr_info(get_opcode(operator), &opproc);
/*
* If we have most-common-values info, add up the fractions of the MCV
* entries that satisfy MCV OP PATTERN. These fractions contribute
* directly to the result selectivity. Also add up the total fraction
* represented by MCV entries.
*/
mcv_selec = mcv_selectivity_cdb(&vardata, &opproc, constval, true,
&sumcommon, &ncommon);
/* CDB: LIKE cannot select fewer rows than "=". */
ndistinct = get_variable_numdistinct(&vardata) - ncommon;
eqsel = 1.0 / Max(1.0, ndistinct);
selec = histogram_selectivity(&vardata, &opproc, constval, true,
100, 1);
if (selec < 0)
{
/* Nope, so fake it with the heuristic method */
selec = pattern_selectivity(rest, ptype);
if (pstatus == Pattern_Prefix_Partial)
{
Selectivity prefixsel;
prefixsel = prefix_selectivity(&vardata, opclass, prefix);
/* CDB: Assume prefix matches at least a few distinct values. */
fewsel = (DEFAULT_RANGE_INEQ_SEL / DEFAULT_EQ_SEL) * eqsel;
prefixsel = Min(Max(prefixsel, fewsel), 1.0);
selec *= prefixsel;
}
else
fewvalues = DEFAULT_MATCH_SEL / DEFAULT_EQ_SEL;
}
/* CDB: Assume whole pattern matches at least a few distinct values. */
fewsel = fewvalues * eqsel;
selec = Min(Max(selec, fewsel), 1.0);
if (HeapTupleIsValid(getStatsTuple(&vardata)))
{
HeapTuple tp = getStatsTuple(&vardata);
nullfrac = ((Form_pg_statistic) GETSTRUCT(tp))->stanullfrac;
}
else
nullfrac = 0.0;
/*
* Now merge the results from the MCV and histogram calculations,
* realizing that the histogram covers only the non-null values that
* are not listed in MCV.
*/
selec *= 1.0 - nullfrac - sumcommon;
selec += mcv_selec;
/* result should be in range, but make sure... */
CLAMP_PROBABILITY(selec);
result = selec;
}
if (prefix)
{
pfree(DatumGetPointer(prefix->constvalue));
pfree(prefix);
}
ReleaseVariableStats(vardata);
return negate ? (1.0 - result) : result;
}
/*
* regexeqsel - Selectivity of regular-expression pattern match.
*/
Datum
regexeqsel(PG_FUNCTION_ARGS)
{
PG_RETURN_FLOAT8(patternsel(fcinfo, Pattern_Type_Regex, false));
}
/*
* icregexeqsel - Selectivity of case-insensitive regex match.
*/
Datum
icregexeqsel(PG_FUNCTION_ARGS)
{
PG_RETURN_FLOAT8(patternsel(fcinfo, Pattern_Type_Regex_IC, false));
}
/*
* likesel - Selectivity of LIKE pattern match.
*/
Datum
likesel(PG_FUNCTION_ARGS)
{
PG_RETURN_FLOAT8(patternsel(fcinfo, Pattern_Type_Like, false));
}
/*
* iclikesel - Selectivity of ILIKE pattern match.
*/
Datum
iclikesel(PG_FUNCTION_ARGS)
{
PG_RETURN_FLOAT8(patternsel(fcinfo, Pattern_Type_Like_IC, false));
}
/*
* regexnesel - Selectivity of regular-expression pattern non-match.
*/
Datum
regexnesel(PG_FUNCTION_ARGS)
{
PG_RETURN_FLOAT8(patternsel(fcinfo, Pattern_Type_Regex, true));
}
/*
* icregexnesel - Selectivity of case-insensitive regex non-match.
*/
Datum
icregexnesel(PG_FUNCTION_ARGS)
{
PG_RETURN_FLOAT8(patternsel(fcinfo, Pattern_Type_Regex_IC, true));
}
/*
* nlikesel - Selectivity of LIKE pattern non-match.
*/
Datum
nlikesel(PG_FUNCTION_ARGS)
{
PG_RETURN_FLOAT8(patternsel(fcinfo, Pattern_Type_Like, true));
}
/*
* icnlikesel - Selectivity of ILIKE pattern non-match.
*/
Datum
icnlikesel(PG_FUNCTION_ARGS)
{
PG_RETURN_FLOAT8(patternsel(fcinfo, Pattern_Type_Like_IC, true));
}
/*
* booltestsel - Selectivity of BooleanTest Node.
*/
Selectivity
booltestsel(PlannerInfo *root, BoolTestType booltesttype, Node *arg,
int varRelid, JoinType jointype)
{
VariableStatData vardata;
double selec;
examine_variable(root, arg, varRelid, &vardata);
if (HeapTupleIsValid(getStatsTuple(&vardata)))
{
Form_pg_statistic stats;
double freq_null;
Datum *values;
int nvalues;
float4 *numbers;
int nnumbers;
HeapTuple tp = getStatsTuple(&vardata);
stats = (Form_pg_statistic) GETSTRUCT(tp);
freq_null = stats->stanullfrac;
if (get_attstatsslot(tp,
vardata.atttype, vardata.atttypmod,
STATISTIC_KIND_MCV, InvalidOid,
&values, &nvalues,
&numbers, &nnumbers)
&& nnumbers > 0)
{
double freq_true;
double freq_false;
/*
* Get first MCV frequency and derive frequency for true.
*/
if (DatumGetBool(values[0]))
freq_true = numbers[0];
else
freq_true = 1.0 - numbers[0] - freq_null;
/*
* Next derive frequency for false. Then use these as appropriate
* to derive frequency for each case.
*/
freq_false = 1.0 - freq_true - freq_null;
switch (booltesttype)
{
case IS_UNKNOWN:
/* select only NULL values */
selec = freq_null;
break;
case IS_NOT_UNKNOWN:
/* select non-NULL values */
selec = 1.0 - freq_null;
break;
case IS_TRUE:
/* select only TRUE values */
selec = freq_true;
break;
case IS_NOT_TRUE:
/* select non-TRUE values */
selec = 1.0 - freq_true;
break;
case IS_FALSE:
/* select only FALSE values */
selec = freq_false;
break;
case IS_NOT_FALSE:
/* select non-FALSE values */
selec = 1.0 - freq_false;
break;
default:
elog(ERROR, "unrecognized booltesttype: %d",
(int) booltesttype);
selec = 0.0; /* Keep compiler quiet */
break;
}
free_attstatsslot(vardata.atttype, values, nvalues,
numbers, nnumbers);
}
else
{
/*
* No most-common-value info available. Still have null fraction
* information, so use it for IS [NOT] UNKNOWN. Otherwise adjust
* for null fraction and assume an even split for boolean tests.
*/
switch (booltesttype)
{
case IS_UNKNOWN:
/*
* Use freq_null directly.
*/
selec = freq_null;
break;
case IS_NOT_UNKNOWN:
/*
* Select not unknown (not null) values. Calculate from
* freq_null.
*/
selec = 1.0 - freq_null;
break;
case IS_TRUE:
case IS_NOT_TRUE:
case IS_FALSE:
case IS_NOT_FALSE:
selec = (1.0 - freq_null) / 2.0;
break;
default:
elog(ERROR, "unrecognized booltesttype: %d",
(int) booltesttype);
selec = 0.0; /* Keep compiler quiet */
break;
}
}
}
else
{
/*
* If we can't get variable statistics for the argument, perhaps
* clause_selectivity can do something with it. We ignore the
* possibility of a NULL value when using clause_selectivity, and just
* assume the value is either TRUE or FALSE.
*/
switch (booltesttype)
{
case IS_UNKNOWN:
selec = DEFAULT_UNK_SEL;
break;
case IS_NOT_UNKNOWN:
selec = DEFAULT_NOT_UNK_SEL;
break;
case IS_TRUE:
case IS_NOT_FALSE:
selec = (double) clause_selectivity(root, arg,
varRelid, jointype,
false /* use_damping */);
break;
case IS_FALSE:
case IS_NOT_TRUE:
selec = 1.0 - (double) clause_selectivity(root, arg,
varRelid, jointype,
false /* use_damping */);
break;
default:
elog(ERROR, "unrecognized booltesttype: %d",
(int) booltesttype);
selec = 0.0; /* Keep compiler quiet */
break;
}
}
ReleaseVariableStats(vardata);
/* result should be in range, but make sure... */
CLAMP_PROBABILITY(selec);
return (Selectivity) selec;
}
/*
* nulltestsel - Selectivity of NullTest Node.
*/
Selectivity
nulltestsel(PlannerInfo *root, NullTestType nulltesttype,
Node *arg, int varRelid, JoinType jointype)
{
VariableStatData vardata;
double selec;
/*
* Special hack: an IS NULL test being applied at an outer join should not
* be taken at face value, since it's very likely being used to select the
* outer-side rows that don't have a match, and thus its selectivity has
* nothing whatever to do with the statistics of the original table
* column. We do not have nearly enough context here to determine its
* true selectivity, so for the moment punt and guess at 0.5. Eventually
* the planner should be made to provide enough info about the clause's
* context to let us do better.
*/
if (IS_OUTER_JOIN(jointype) && nulltesttype == IS_NULL)
return (Selectivity) 0.5;
examine_variable(root, arg, varRelid, &vardata);
if (HeapTupleIsValid(getStatsTuple(&vardata)))
{
Form_pg_statistic stats;
HeapTuple tp = getStatsTuple(&vardata);
double freq_null;
stats = (Form_pg_statistic) GETSTRUCT(tp);
freq_null = stats->stanullfrac;
switch (nulltesttype)
{
case IS_NULL:
/*
* Use freq_null directly.
*/
selec = freq_null;
break;
case IS_NOT_NULL:
/*
* Select not unknown (not null) values. Calculate from
* freq_null.
*/
selec = 1.0 - freq_null;
break;
default:
elog(ERROR, "unrecognized nulltesttype: %d",
(int) nulltesttype);
return (Selectivity) 0; /* keep compiler quiet */
}
}
else
{
/*
* No ANALYZE stats available, so make a guess
*/
switch (nulltesttype)
{
case IS_NULL:
selec = DEFAULT_UNK_SEL;
break;
case IS_NOT_NULL:
selec = DEFAULT_NOT_UNK_SEL;
break;
default:
elog(ERROR, "unrecognized nulltesttype: %d",
(int) nulltesttype);
return (Selectivity) 0; /* keep compiler quiet */
}
}
ReleaseVariableStats(vardata);
/* result should be in range, but make sure... */
CLAMP_PROBABILITY(selec);
return (Selectivity) selec;
}
/*
* strip_array_coercion - strip binary-compatible relabeling from an array expr
*
* For array values, the parser doesn't generate simple RelabelType nodes,
* but function calls of array_type_coerce() or array_type_length_coerce().
* If we want to cope with binary-compatible situations we have to look
* through these calls whenever the element-type coercion is binary-compatible.
*/
static Node *
strip_array_coercion(Node *node)
{
/* could be more than one level, so loop */
for (;;)
{
if (node && IsA(node, RelabelType))
{
/* We don't really expect this case, but may as well cope */
node = (Node *) ((RelabelType *) node)->arg;
}
else if (node && IsA(node, FuncExpr))
{
FuncExpr *fexpr = (FuncExpr *) node;
Node *arg1;
Oid src_elem_type;
Oid tgt_elem_type;
Oid funcId;
/* must be the right function(s) */
if (!(fexpr->funcid == F_ARRAY_TYPE_COERCE ||
fexpr->funcid == F_ARRAY_TYPE_LENGTH_COERCE))
break;
/* fetch source and destination array element types */
arg1 = (Node *) linitial(fexpr->args);
src_elem_type = get_element_type(exprType(arg1));
if (src_elem_type == InvalidOid)
break; /* probably shouldn't happen */
tgt_elem_type = get_element_type(fexpr->funcresulttype);
if (tgt_elem_type == InvalidOid)
break; /* probably shouldn't happen */
/* find out how to coerce */
if (!find_coercion_pathway(tgt_elem_type, src_elem_type,
COERCION_EXPLICIT, &funcId))
break; /* definitely shouldn't happen */
if (OidIsValid(funcId))
break; /* non-binary-compatible coercion */
node = arg1; /* OK to look through the node */
}
else
break;
}
return node;
}
/*
* scalararraysel - Selectivity of ScalarArrayOpExpr Node.
*/
Selectivity
scalararraysel(PlannerInfo *root,
ScalarArrayOpExpr *clause,
bool is_join_clause,
int varRelid, JoinType jointype)
{
Oid operator = clause->opno;
bool useOr = clause->useOr;
Node *leftop;
Node *rightop;
Oid nominal_element_type;
RegProcedure oprsel;
FmgrInfo oprselproc;
Datum selarg4;
Selectivity s1;
/*
* First, look up the underlying operator's selectivity estimator. Punt if
* it hasn't got one.
*/
if (is_join_clause)
{
oprsel = get_oprjoin(operator);
selarg4 = Int16GetDatum(jointype);
}
else
{
oprsel = get_oprrest(operator);
selarg4 = Int32GetDatum(varRelid);
}
if (!oprsel)
return (Selectivity) 0.5;
fmgr_info(oprsel, &oprselproc);
/* deconstruct the expression */
Assert(list_length(clause->args) == 2);
leftop = (Node *) linitial(clause->args);
rightop = (Node *) lsecond(clause->args);
/* get nominal (after relabeling) element type of rightop */
nominal_element_type = get_element_type(exprType(rightop));
if (!OidIsValid(nominal_element_type))
return (Selectivity) 0.5; /* probably shouldn't happen */
/* look through any binary-compatible relabeling of rightop */
rightop = strip_array_coercion(rightop);
/*
* We consider three cases:
*
* 1. rightop is an Array constant: deconstruct the array, apply the
* operator's selectivity function for each array element, and merge the
* results in the same way that clausesel.c does for AND/OR combinations.
*
* 2. rightop is an ARRAY[] construct: apply the operator's selectivity
* function for each element of the ARRAY[] construct, and merge.
*
* 3. otherwise, make a guess ...
*/
if (rightop && IsA(rightop, Const))
{
Datum arraydatum = ((Const *) rightop)->constvalue;
bool arrayisnull = ((Const *) rightop)->constisnull;
ArrayType *arrayval;
int16 elmlen;
bool elmbyval;
char elmalign;
int num_elems;
Datum *elem_values;
bool *elem_nulls;
int i;
if (arrayisnull) /* qual can't succeed if null array */
return (Selectivity) 0.0;
arrayval = DatumGetArrayTypeP(arraydatum);
get_typlenbyvalalign(ARR_ELEMTYPE(arrayval),
&elmlen, &elmbyval, &elmalign);
deconstruct_array(arrayval,
ARR_ELEMTYPE(arrayval),
elmlen, elmbyval, elmalign,
&elem_values, &elem_nulls, &num_elems);
s1 = useOr ? 0.0 : 1.0;
for (i = 0; i < num_elems; i++)
{
List *args;
Selectivity s2;
args = list_make2(leftop,
makeConst(nominal_element_type, -1,
elmlen,
elem_values[i],
elem_nulls[i],
elmbyval));
s2 = DatumGetFloat8(FunctionCall4(&oprselproc,
PointerGetDatum(root),
ObjectIdGetDatum(operator),
PointerGetDatum(args),
selarg4));
if (useOr)
s1 = s1 + s2 - s1 * s2;
else
s1 = s1 * s2;
}
}
else if (rightop && IsA(rightop, ArrayExpr) &&
!((ArrayExpr *) rightop)->multidims)
{
ArrayExpr *arrayexpr = (ArrayExpr *) rightop;
int16 elmlen;
bool elmbyval;
ListCell *l;
get_typlenbyval(arrayexpr->element_typeid,
&elmlen, &elmbyval);
s1 = useOr ? 0.0 : 1.0;
foreach(l, arrayexpr->elements)
{
Node *elem = (Node *) lfirst(l);
List *args;
Selectivity s2;
/*
* Theoretically, if elem isn't of nominal_element_type we should
* insert a RelabelType, but it seems unlikely that any operator
* estimation function would really care ...
*/
args = list_make2(leftop, elem);
s2 = DatumGetFloat8(FunctionCall4(&oprselproc,
PointerGetDatum(root),
ObjectIdGetDatum(operator),
PointerGetDatum(args),
selarg4));
if (useOr)
s1 = s1 + s2 - s1 * s2;
else
s1 = s1 * s2;
}
}
else
{
CaseTestExpr *dummyexpr;
List *args;
Selectivity s2;
int i;
/*
* We need a dummy rightop to pass to the operator selectivity
* routine. It can be pretty much anything that doesn't look like a
* constant; CaseTestExpr is a convenient choice.
*/
dummyexpr = makeNode(CaseTestExpr);
dummyexpr->typeId = nominal_element_type;
dummyexpr->typeMod = -1;
args = list_make2(leftop, dummyexpr);
s2 = DatumGetFloat8(FunctionCall4(&oprselproc,
PointerGetDatum(root),
ObjectIdGetDatum(operator),
PointerGetDatum(args),
selarg4));
s1 = useOr ? 0.0 : 1.0;
/*
* Arbitrarily assume 10 elements in the eventual array value (see
* also estimate_array_length)
*/
for (i = 0; i < 10; i++)
{
if (useOr)
s1 = s1 + s2 - s1 * s2;
else
s1 = s1 * s2;
}
}
/* result should be in range, but make sure... */
CLAMP_PROBABILITY(s1);
return s1;
}
/*
* Estimate number of elements in the array yielded by an expression.
*
* It's important that this agree with scalararraysel.
*/
int
estimate_array_length(Node *arrayexpr)
{
/* look through any binary-compatible relabeling of arrayexpr */
arrayexpr = strip_array_coercion(arrayexpr);
if (arrayexpr && IsA(arrayexpr, Const))
{
Datum arraydatum = ((Const *) arrayexpr)->constvalue;
bool arrayisnull = ((Const *) arrayexpr)->constisnull;
ArrayType *arrayval;
if (arrayisnull)
return 0;
arrayval = DatumGetArrayTypeP(arraydatum);
return ArrayGetNItems(ARR_NDIM(arrayval), ARR_DIMS(arrayval));
}
else if (arrayexpr && IsA(arrayexpr, ArrayExpr) &&
!((ArrayExpr *) arrayexpr)->multidims)
{
return list_length(((ArrayExpr *) arrayexpr)->elements);
}
else
{
/* default guess --- see also scalararraysel */
return 10;
}
}
/*
* rowcomparesel - Selectivity of RowCompareExpr Node.
*
* We estimate RowCompare selectivity by considering just the first (high
* order) columns, which makes it equivalent to an ordinary OpExpr. While
* this estimate could be refined by considering additional columns, it
* seems unlikely that we could do a lot better without multi-column
* statistics.
*/
Selectivity
rowcomparesel(PlannerInfo *root,
RowCompareExpr *clause,
int varRelid, JoinType jointype)
{
Selectivity s1;
Oid opno = linitial_oid(clause->opnos);
List *opargs;
bool is_join_clause;
/* Build equivalent arg list for single operator */
opargs = list_make2(linitial(clause->largs), linitial(clause->rargs));
/* Decide if it's a join clause, same as for OpExpr */
if (varRelid != 0)
{
/*
* If we are considering a nestloop join then all clauses are
* restriction clauses, since we are only interested in the one
* relation.
*/
is_join_clause = false;
}
else
{
/*
* Otherwise, it's a join if there's more than one relation used.
* Notice we ignore the low-order columns here.
*/
is_join_clause = (NumRelids((Node *) opargs) > 1);
}
if (is_join_clause)
{
/* Estimate selectivity for a join clause. */
s1 = join_selectivity(root, opno,
opargs,
jointype);
}
else
{
/* Estimate selectivity for a restriction clause. */
s1 = restriction_selectivity(root, opno,
opargs,
varRelid);
}
return s1;
}
/*
* eqjoinsel - Join selectivity of "="
*/
Datum
eqjoinsel(PG_FUNCTION_ARGS)
{
PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0);
Oid operator = PG_GETARG_OID(1);
List *args = (List *) PG_GETARG_POINTER(2);
JoinType jointype = (JoinType) PG_GETARG_INT16(3);
double selec;
VariableStatData vardata1;
VariableStatData vardata2;
double nd1;
double nd2;
Form_pg_statistic stats1 = NULL;
Form_pg_statistic stats2 = NULL;
bool have_mcvs1 = false;
Datum *values1 = NULL;
int nvalues1 = 0;
float4 *numbers1 = NULL;
int nnumbers1 = 0;
bool have_mcvs2 = false;
Datum *values2 = NULL;
int nvalues2 = 0;
float4 *numbers2 = NULL;
int nnumbers2 = 0;
get_join_variables(root, args, &vardata1, &vardata2);
nd1 = get_variable_numdistinct(&vardata1);
nd2 = get_variable_numdistinct(&vardata2);
if (HeapTupleIsValid(getStatsTuple(&vardata1)))
{
HeapTuple tp = getStatsTuple(&vardata1);
stats1 = (Form_pg_statistic) GETSTRUCT(tp);
have_mcvs1 = get_attstatsslot(tp,
vardata1.atttype,
vardata1.atttypmod,
STATISTIC_KIND_MCV,
InvalidOid,
&values1, &nvalues1,
&numbers1, &nnumbers1);
}
if (HeapTupleIsValid(getStatsTuple(&vardata2)))
{
HeapTuple tp = getStatsTuple(&vardata2);
stats2 = (Form_pg_statistic) GETSTRUCT(tp);
have_mcvs2 = get_attstatsslot(tp,
vardata2.atttype,
vardata2.atttypmod,
STATISTIC_KIND_MCV,
InvalidOid,
&values2, &nvalues2,
&numbers2, &nnumbers2);
}
if (have_mcvs1 && have_mcvs2)
{
/*
* We have most-common-value lists for both relations. Run through
* the lists to see which MCVs actually join to each other with the
* given operator. This allows us to determine the exact join
* selectivity for the portion of the relations represented by the MCV
* lists. We still have to estimate for the remaining population, but
* in a skewed distribution this gives us a big leg up in accuracy.
* For motivation see the analysis in Y. Ioannidis and S.
* Christodoulakis, "On the propagation of errors in the size of join
* results", Technical Report 1018, Computer Science Dept., University
* of Wisconsin, Madison, March 1991 (available from ftp.cs.wisc.edu).
*/
FmgrInfo eqproc;
bool *hasmatch1;
bool *hasmatch2;
double nullfrac1 = stats1->stanullfrac;
double nullfrac2 = stats2->stanullfrac;
double matchprodfreq,
matchfreq1,
matchfreq2,
unmatchfreq1,
unmatchfreq2,
otherfreq1,
otherfreq2,
totalsel1,
totalsel2;
int i,
nmatches;
fmgr_info(get_opcode(operator), &eqproc);
hasmatch1 = (bool *) palloc0(nvalues1 * sizeof(bool));
hasmatch2 = (bool *) palloc0(nvalues2 * sizeof(bool));
/*
* If we are doing any variant of JOIN_IN, pretend all the values of
* the righthand relation are unique (ie, act as if it's been
* DISTINCT'd).
*
* NOTE: it might seem that we should unique-ify the lefthand input
* when considering JOIN_REVERSE_IN. But this is not so, because the
* join clause we've been handed has not been commuted from the way
* the parser originally wrote it. We know that the unique side of
* the IN clause is *always* on the right.
*
* NOTE: it would be dangerous to try to be smart about JOIN_LEFT or
* JOIN_RIGHT here, because we do not have enough information to
* determine which var is really on which side of the join. Perhaps
* someday we should pass in more information.
*/
if (jointype == JOIN_IN)
{
float4 oneovern = 1.0 / nd2;
for (i = 0; i < nvalues2; i++)
numbers2[i] = oneovern;
nullfrac2 = oneovern;
}
/*
* Note we assume that each MCV will match at most one member of the
* other MCV list. If the operator isn't really equality, there could
* be multiple matches --- but we don't look for them, both for speed
* and because the math wouldn't add up...
*/
matchprodfreq = 0.0;
nmatches = 0;
for (i = 0; i < nvalues1; i++)
{
int j;
for (j = 0; j < nvalues2; j++)
{
if (hasmatch2[j])
continue;
if (DatumGetBool(FunctionCall2(&eqproc,
values1[i],
values2[j])))
{
hasmatch1[i] = hasmatch2[j] = true;
matchprodfreq += numbers1[i] * numbers2[j];
nmatches++;
break;
}
}
}
CLAMP_PROBABILITY(matchprodfreq);
/* Sum up frequencies of matched and unmatched MCVs */
matchfreq1 = unmatchfreq1 = 0.0;
for (i = 0; i < nvalues1; i++)
{
if (hasmatch1[i])
matchfreq1 += numbers1[i];
else
unmatchfreq1 += numbers1[i];
}
CLAMP_PROBABILITY(matchfreq1);
CLAMP_PROBABILITY(unmatchfreq1);
matchfreq2 = unmatchfreq2 = 0.0;
for (i = 0; i < nvalues2; i++)
{
if (hasmatch2[i])
matchfreq2 += numbers2[i];
else
unmatchfreq2 += numbers2[i];
}
CLAMP_PROBABILITY(matchfreq2);
CLAMP_PROBABILITY(unmatchfreq2);
pfree(hasmatch1);
pfree(hasmatch2);
/*
* Compute total frequency of non-null values that are not in the MCV
* lists.
*/
otherfreq1 = 1.0 - nullfrac1 - matchfreq1 - unmatchfreq1;
otherfreq2 = 1.0 - nullfrac2 - matchfreq2 - unmatchfreq2;
CLAMP_PROBABILITY(otherfreq1);
CLAMP_PROBABILITY(otherfreq2);
/*
* We can estimate the total selectivity from the point of view of
* relation 1 as: the known selectivity for matched MCVs, plus
* unmatched MCVs that are assumed to match against random members of
* relation 2's non-MCV population, plus non-MCV values that are
* assumed to match against random members of relation 2's unmatched
* MCVs plus non-MCV values.
*/
totalsel1 = matchprodfreq;
if (nd2 > nvalues2)
totalsel1 += unmatchfreq1 * otherfreq2 / (nd2 - nvalues2);
if (nd2 > nmatches)
totalsel1 += otherfreq1 * (otherfreq2 + unmatchfreq2) /
(nd2 - nmatches);
/* Same estimate from the point of view of relation 2. */
totalsel2 = matchprodfreq;
if (nd1 > nvalues1)
totalsel2 += unmatchfreq2 * otherfreq1 / (nd1 - nvalues1);
if (nd1 > nmatches)
totalsel2 += otherfreq2 * (otherfreq1 + unmatchfreq1) /
(nd1 - nmatches);
/*
* Use the smaller of the two estimates. This can be justified in
* essentially the same terms as given below for the no-stats case: to
* a first approximation, we are estimating from the point of view of
* the relation with smaller nd.
*/
selec = (totalsel1 < totalsel2) ? totalsel1 : totalsel2;
}
else
{
/*
* We do not have MCV lists for both sides. Estimate the join
* selectivity as MIN(1/nd1,1/nd2)*(1-nullfrac1)*(1-nullfrac2). This
* is plausible if we assume that the join operator is strict and the
* non-null values are about equally distributed: a given non-null
* tuple of rel1 will join to either zero or N2*(1-nullfrac2)/nd2 rows
* of rel2, so total join rows are at most
* N1*(1-nullfrac1)*N2*(1-nullfrac2)/nd2 giving a join selectivity of
* not more than (1-nullfrac1)*(1-nullfrac2)/nd2. By the same logic it
* is not more than (1-nullfrac1)*(1-nullfrac2)/nd1, so the expression
* with MIN() is an upper bound. Using the MIN() means we estimate
* from the point of view of the relation with smaller nd (since the
* larger nd is determining the MIN). It is reasonable to assume that
* most tuples in this rel will have join partners, so the bound is
* probably reasonably tight and should be taken as-is.
*
* XXX Can we be smarter if we have an MCV list for just one side? It
* seems that if we assume equal distribution for the other side, we
* end up with the same answer anyway.
*/
double nullfrac1 = stats1 ? stats1->stanullfrac : 0.0;
double nullfrac2 = stats2 ? stats2->stanullfrac : 0.0;
selec = (1.0 - nullfrac1) * (1.0 - nullfrac2);
if (nd1 > nd2)
selec /= nd1;
else
selec /= nd2;
}
if (have_mcvs1)
free_attstatsslot(vardata1.atttype, values1, nvalues1,
numbers1, nnumbers1);
if (have_mcvs2)
free_attstatsslot(vardata2.atttype, values2, nvalues2,
numbers2, nnumbers2);
ReleaseVariableStats(vardata1);
ReleaseVariableStats(vardata2);
CLAMP_PROBABILITY(selec);
PG_RETURN_FLOAT8((float8) selec);
}
/*
* neqjoinsel - Join selectivity of "!="
*/
Datum
neqjoinsel(PG_FUNCTION_ARGS)
{
PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0);
Oid operator = PG_GETARG_OID(1);
List *args = (List *) PG_GETARG_POINTER(2);
JoinType jointype = (JoinType) PG_GETARG_INT16(3);
Oid eqop;
float8 result;
/*
* We want 1 - eqjoinsel() where the equality operator is the one
* associated with this != operator, that is, its negator.
*/
eqop = get_negator(operator);
if (eqop)
{
result = DatumGetFloat8(DirectFunctionCall4(eqjoinsel,
PointerGetDatum(root),
ObjectIdGetDatum(eqop),
PointerGetDatum(args),
Int16GetDatum(jointype)));
}
else
{
/* Use default selectivity (should we raise an error instead?) */
result = DEFAULT_EQ_SEL;
}
result = 1.0 - result;
PG_RETURN_FLOAT8(result);
}
/*
* scalarltjoinsel - Join selectivity of "<" and "<=" for scalars
*/
Datum
scalarltjoinsel(PG_FUNCTION_ARGS)
{
PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
}
/*
* scalargtjoinsel - Join selectivity of ">" and ">=" for scalars
*/
Datum
scalargtjoinsel(PG_FUNCTION_ARGS)
{
PG_RETURN_FLOAT8(DEFAULT_INEQ_SEL);
}
/*
* patternjoinsel - Generic code for pattern-match join selectivity.
*/
static double
patternjoinsel(PG_FUNCTION_ARGS, Pattern_Type ptype, bool negate)
{
/* For the moment we just punt. */
return negate ? (1.0 - DEFAULT_MATCH_SEL) : DEFAULT_MATCH_SEL;
}
/*
* regexeqjoinsel - Join selectivity of regular-expression pattern match.
*/
Datum
regexeqjoinsel(PG_FUNCTION_ARGS)
{
PG_RETURN_FLOAT8(patternjoinsel(fcinfo, Pattern_Type_Regex, false));
}
/*
* icregexeqjoinsel - Join selectivity of case-insensitive regex match.
*/
Datum
icregexeqjoinsel(PG_FUNCTION_ARGS)
{
PG_RETURN_FLOAT8(patternjoinsel(fcinfo, Pattern_Type_Regex_IC, false));
}
/*
* likejoinsel - Join selectivity of LIKE pattern match.
*/
Datum
likejoinsel(PG_FUNCTION_ARGS)
{
PG_RETURN_FLOAT8(patternjoinsel(fcinfo, Pattern_Type_Like, false));
}
/*
* iclikejoinsel - Join selectivity of ILIKE pattern match.
*/
Datum
iclikejoinsel(PG_FUNCTION_ARGS)
{
PG_RETURN_FLOAT8(patternjoinsel(fcinfo, Pattern_Type_Like_IC, false));
}
/*
* regexnejoinsel - Join selectivity of regex non-match.
*/
Datum
regexnejoinsel(PG_FUNCTION_ARGS)
{
PG_RETURN_FLOAT8(patternjoinsel(fcinfo, Pattern_Type_Regex, true));
}
/*
* icregexnejoinsel - Join selectivity of case-insensitive regex non-match.
*/
Datum
icregexnejoinsel(PG_FUNCTION_ARGS)
{
PG_RETURN_FLOAT8(patternjoinsel(fcinfo, Pattern_Type_Regex_IC, true));
}
/*
* nlikejoinsel - Join selectivity of LIKE pattern non-match.
*/
Datum
nlikejoinsel(PG_FUNCTION_ARGS)
{
PG_RETURN_FLOAT8(patternjoinsel(fcinfo, Pattern_Type_Like, true));
}
/*
* icnlikejoinsel - Join selectivity of ILIKE pattern non-match.
*/
Datum
icnlikejoinsel(PG_FUNCTION_ARGS)
{
PG_RETURN_FLOAT8(patternjoinsel(fcinfo, Pattern_Type_Like_IC, true));
}
/*
* mergejoinscansel - Scan selectivity of merge join.
*
* A merge join will stop as soon as it exhausts either input stream.
* Therefore, if we can estimate the ranges of both input variables,
* we can estimate how much of the input will actually be read. This
* can have a considerable impact on the cost when using indexscans.
*
* clause should be a clause already known to be mergejoinable.
*
* *leftscan is set to the fraction of the left-hand variable expected
* to be scanned (0 to 1), and similarly *rightscan for the right-hand
* variable.
*/
void
mergejoinscansel(PlannerInfo *root, Node *clause,
Selectivity *leftscan,
Selectivity *rightscan)
{
Node *left,
*right;
VariableStatData leftvar,
rightvar;
Oid lefttype,
righttype;
Oid opno,
lsortop,
rsortop,
ltop,
gtop,
leop,
revgtop,
revleop;
Datum leftmax = 0,
rightmax = 0;
double selec;
/* Set default results if we can't figure anything out. */
*leftscan = *rightscan = 1.0;
/* Deconstruct the merge clause */
if (!is_opclause(clause))
return; /* shouldn't happen */
opno = ((OpExpr *) clause)->opno;
left = get_leftop((Expr *) clause);
right = get_rightop((Expr *) clause);
if (!right)
return; /* shouldn't happen */
/* Look for stats for the inputs */
examine_variable(root, left, 0, &leftvar);
examine_variable(root, right, 0, &rightvar);
/* Get the direct input types of the operator */
lefttype = exprType(left);
righttype = exprType(right);
/* Verify mergejoinability and get left and right "<" operators */
if (!op_mergejoinable(opno,
&lsortop,
&rsortop))
goto fail; /* shouldn't happen */
/* Try to get maximum values of both inputs */
if (!get_variable_maximum(root, &leftvar, lsortop, &leftmax))
goto fail; /* no max available from stats */
if (!get_variable_maximum(root, &rightvar, rsortop, &rightmax))
goto fail; /* no max available from stats */
/* Look up the "left < right" and "left > right" operators */
op_mergejoin_crossops(opno, &ltop, &gtop, NULL, NULL);
/* Look up the "left <= right" operator */
leop = get_negator(gtop);
if (!OidIsValid(leop))
goto fail; /* insufficient info in catalogs */
/* Look up the "right > left" operator */
revgtop = get_commutator(ltop);
if (!OidIsValid(revgtop))
goto fail; /* insufficient info in catalogs */
/* Look up the "right <= left" operator */
revleop = get_negator(revgtop);
if (!OidIsValid(revleop))
goto fail; /* insufficient info in catalogs */
/*
* Now, the fraction of the left variable that will be scanned is the
* fraction that's <= the right-side maximum value. But only believe
* non-default estimates, else stick with our 1.0.
*/
selec = scalarineqsel(root, leop, false, &leftvar,
rightmax, righttype);
if (selec != DEFAULT_INEQ_SEL)
*leftscan = selec;
/* And similarly for the right variable. */
selec = scalarineqsel(root, revleop, false, &rightvar,
leftmax, lefttype);
if (selec != DEFAULT_INEQ_SEL)
*rightscan = selec;
/*
* Only one of the two fractions can really be less than 1.0; believe the
* smaller estimate and reset the other one to exactly 1.0. If we get
* exactly equal estimates (as can easily happen with self-joins), believe
* neither.
*/
if (*leftscan > *rightscan)
*leftscan = 1.0;
else if (*leftscan < *rightscan)
*rightscan = 1.0;
else
*leftscan = *rightscan = 1.0;
fail:
ReleaseVariableStats(leftvar);
ReleaseVariableStats(rightvar);
}
/*
* Helper routine for estimate_num_groups: add an item to a list of
* GroupVarInfos, but only if it's not known equal to any of the existing
* entries.
*/
typedef struct
{
Node *var; /* might be an expression, not just a Var */
RelOptInfo *rel; /* relation it belongs to */
double ndistinct; /* # distinct values */
} GroupVarInfo;
static List *
add_unique_group_var(PlannerInfo *root, List *varinfos,
Node *var, VariableStatData *vardata)
{
GroupVarInfo *varinfo;
double ndistinct;
ListCell *lc;
ndistinct = get_variable_numdistinct(vardata);
/* cannot use foreach here because of possible list_delete */
lc = list_head(varinfos);
while (lc)
{
varinfo = (GroupVarInfo *) lfirst(lc);
/* must advance lc before list_delete possibly pfree's it */
lc = lnext(lc);
/* Drop exact duplicates */
if (equal(var, varinfo->var))
return varinfos;
/*
* Drop known-equal vars, but only if they belong to different
* relations (see comments for estimate_num_groups)
*/
if (vardata->rel != varinfo->rel &&
exprs_known_equal(root, var, varinfo->var))
{
if (varinfo->ndistinct <= ndistinct)
{
/* Keep older item, forget new one */
return varinfos;
}
else
{
/* Delete the older item */
varinfos = list_delete_ptr(varinfos, varinfo);
}
}
}
varinfo = (GroupVarInfo *) palloc(sizeof(GroupVarInfo));
varinfo->var = var;
varinfo->rel = vardata->rel;
varinfo->ndistinct = ndistinct;
varinfos = lappend(varinfos, varinfo);
return varinfos;
}
/*
* estimate_num_groups - Estimate number of groups in a grouped query
*
* Given a query having a GROUP BY clause, estimate how many groups there
* will be --- ie, the number of distinct combinations of the GROUP BY
* expressions.
*
* This routine is also used to estimate the number of rows emitted by
* a DISTINCT filtering step; that is an isomorphic problem. (Note:
* actually, we only use it for DISTINCT when there's no grouping or
* aggregation ahead of the DISTINCT.)
*
* Inputs:
* root - the query
* groupExprs - list of expressions being grouped by
* input_rows - number of rows estimated to arrive at the group/unique
* filter step
*
* Given the lack of any cross-correlation statistics in the system, it's
* impossible to do anything really trustworthy with GROUP BY conditions
* involving multiple Vars. We should however avoid assuming the worst
* case (all possible cross-product terms actually appear as groups) since
* very often the grouped-by Vars are highly correlated. Our current approach
* is as follows:
* 1. Reduce the given expressions to a list of unique Vars used. For
* example, GROUP BY a, a + b is treated the same as GROUP BY a, b.
* It is clearly correct not to count the same Var more than once.
* It is also reasonable to treat f(x) the same as x: f() cannot
* increase the number of distinct values (unless it is volatile,
* which we consider unlikely for grouping), but it probably won't
* reduce the number of distinct values much either.
* As a special case, if a GROUP BY expression can be matched to an
* expressional index for which we have statistics, then we treat the
* whole expression as though it were just a Var.
* 2. If the list contains Vars of different relations that are known equal
* due to equijoin clauses, then drop all but one of the Vars from each
* known-equal set, keeping the one with smallest estimated # of values
* (since the extra values of the others can't appear in joined rows).
* Note the reason we only consider Vars of different relations is that
* if we considered ones of the same rel, we'd be double-counting the
* restriction selectivity of the equality in the next step.
* 3. For Vars within a single source rel, we multiply together the numbers
* of values, clamp to the number of rows in the rel (divided by 10 if
* more than one Var), and then multiply by the selectivity of the
* restriction clauses for that rel. When there's more than one Var,
* the initial product is probably too high (it's the worst case) but
* clamping to a fraction of the rel's rows seems to be a helpful
* heuristic for not letting the estimate get out of hand. (The factor
* of 10 is derived from pre-Postgres-7.4 practice.) Multiplying
* by the restriction selectivity is effectively assuming that the
* restriction clauses are independent of the grouping, which is a crummy
* assumption, but it's hard to do better.
* 4. If there are Vars from multiple rels, we repeat step 3 for each such
* rel, and multiply the results together.
* Note that rels not containing grouped Vars are ignored completely, as are
* join clauses other than the equijoin clauses used in step 2. Such rels
* cannot increase the number of groups, and we assume such clauses do not
* reduce the number either (somewhat bogus, but we don't have the info to
* do better).
*/
double
estimate_num_groups(PlannerInfo *root, List *groupExprs, double input_rows)
{
List *varinfos = NIL;
double numdistinct;
ListCell *l;
/* We should not be called unless query has GROUP BY (or DISTINCT) */
Assert(groupExprs != NIL);
/*
* Steps 1/2: find the unique Vars used, treating an expression as a Var
* if we can find stats for it. For each one, record the statistical
* estimate of number of distinct values (total in its table, without
* regard for filtering).
*/
foreach(l, groupExprs)
{
Node *groupexpr = (Node *) lfirst(l);
VariableStatData vardata;
List *varshere;
ListCell *l2;
/*
* If examine_variable is able to deduce anything about the GROUP BY
* expression, treat it as a single variable even if it's really more
* complicated.
*/
examine_variable(root, groupexpr, 0, &vardata);
if (HeapTupleIsValid(getStatsTuple(&vardata))
|| vardata.isunique)
{
varinfos = add_unique_group_var(root, varinfos,
groupexpr, &vardata);
ReleaseVariableStats(vardata);
continue;
}
ReleaseVariableStats(vardata);
/*
* Else pull out the component Vars
*/
varshere = pull_var_clause(groupexpr, false);
/*
* If we find any variable-free GROUP BY item, then either it is a
* constant (and we can ignore it) or it contains a volatile function;
* in the latter case we punt and assume that each input row will
* yield a distinct group.
*/
if (varshere == NIL)
{
if (contain_volatile_functions(groupexpr))
return input_rows;
continue;
}
/*
* Else add variables to varinfos list
*/
foreach(l2, varshere)
{
Node *var = (Node *) lfirst(l2);
examine_variable(root, var, 0, &vardata);
varinfos = add_unique_group_var(root, varinfos, var, &vardata);
ReleaseVariableStats(vardata);
}
}
/* If now no Vars, we must have an all-constant GROUP BY list. */
if (varinfos == NIL)
return 1.0;
/*
* Steps 3/4: group Vars by relation and estimate total numdistinct.
*
* For each iteration of the outer loop, we process the frontmost Var in
* varinfos, plus all other Vars in the same relation. We remove these
* Vars from the newvarinfos list for the next iteration. This is the
* easiest way to group Vars of same rel together.
*/
numdistinct = 1.0;
do
{
GroupVarInfo *varinfo1 = (GroupVarInfo *) linitial(varinfos);
RelOptInfo *rel = varinfo1->rel;
double reldistinct = varinfo1->ndistinct;
double relmaxndistinct = reldistinct;
int relvarcount = 1;
List *newvarinfos = NIL;
/*
* Get the product of numdistinct estimates of the Vars for this rel.
* Also, construct new varinfos list of remaining Vars.
*/
for_each_cell(l, lnext(list_head(varinfos)))
{
GroupVarInfo *varinfo2 = (GroupVarInfo *) lfirst(l);
if (varinfo2->rel == varinfo1->rel)
{
reldistinct *= varinfo2->ndistinct;
if (relmaxndistinct < varinfo2->ndistinct)
relmaxndistinct = varinfo2->ndistinct;
relvarcount++;
}
else
{
/* not time to process varinfo2 yet */
newvarinfos = lcons(varinfo2, newvarinfos);
}
}
/*
* Sanity check --- don't divide by zero if empty relation.
*/
Assert(rel->reloptkind == RELOPT_BASEREL ||
(rel->reloptkind == RELOPT_OTHER_MEMBER_REL &&
rel->rtekind == RTE_RELATION));
if (rel->tuples > 0)
{
/*
* Clamp to size of rel, or size of rel / 10 if multiple Vars. The
* fudge factor is because the Vars are probably correlated but we
* don't know by how much. We should never clamp to less than the
* largest ndistinct value for any of the Vars, though, since
* there will surely be at least that many groups.
*/
double clamp = rel->tuples;
if (relvarcount > 1)
{
clamp *= 0.1;
if (clamp < relmaxndistinct)
{
clamp = relmaxndistinct;
/* for sanity in case some ndistinct is too large: */
if (clamp > rel->tuples)
clamp = rel->tuples;
}
}
if (reldistinct > clamp)
reldistinct = clamp;
/*
* Multiply by restriction selectivity.
*/
reldistinct *= rel->rows / rel->tuples;
/*
* Update estimate of total distinct groups.
*/
numdistinct *= reldistinct;
}
varinfos = newvarinfos;
} while (varinfos != NIL);
numdistinct = ceil(numdistinct);
/* Guard against out-of-range answers */
if (numdistinct > input_rows)
numdistinct = input_rows;
if (numdistinct < 1.0)
numdistinct = 1.0;
return numdistinct;
}
/*
* Estimate hash bucketsize fraction (ie, number of entries in a bucket
* divided by total tuples in relation) if the specified expression is used
* as a hash key.
*
* XXX This is really pretty bogus since we're effectively assuming that the
* distribution of hash keys will be the same after applying restriction
* clauses as it was in the underlying relation. However, we are not nearly
* smart enough to figure out how the restrict clauses might change the
* distribution, so this will have to do for now.
*
* We are passed the number of buckets the executor will use for the given
* input relation. If the data were perfectly distributed, with the same
* number of tuples going into each available bucket, then the bucketsize
* fraction would be 1/nbuckets. But this happy state of affairs will occur
* only if (a) there are at least nbuckets distinct data values, and (b)
* we have a not-too-skewed data distribution. Otherwise the buckets will
* be nonuniformly occupied. If the other relation in the join has a key
* distribution similar to this one's, then the most-loaded buckets are
* exactly those that will be probed most often. Therefore, the "average"
* bucket size for costing purposes should really be taken as something close
* to the "worst case" bucket size. We try to estimate this by adjusting the
* fraction if there are too few distinct data values, and then scaling up
* by the ratio of the most common value's frequency to the average frequency.
*
* If no statistics are available, use a default estimate of 0.1. This will
* discourage use of a hash rather strongly if the inner relation is large,
* which is what we want. We do not want to hash unless we know that the
* inner rel is well-dispersed (or the alternatives seem much worse).
*/
Selectivity
estimate_hash_bucketsize(PlannerInfo *root, Node *hashkey, double nbuckets)
{
VariableStatData vardata;
double estfract,
ndistinct,
stanullfrac,
mcvfreq,
avgfreq;
float4 *numbers;
int nnumbers;
examine_variable(root, hashkey, 0, &vardata);
/* Get number of distinct values and fraction that are null */
ndistinct = get_variable_numdistinct(&vardata);
if (HeapTupleIsValid(getStatsTuple(&vardata)))
{
HeapTuple tp = getStatsTuple(&vardata);
Form_pg_statistic stats;
stats = (Form_pg_statistic) GETSTRUCT(tp);
stanullfrac = stats->stanullfrac;
}
else
{
/*
* Believe a default ndistinct only if it came from stats. Otherwise
* punt and return 0.1, per comments above.
*/
if (ndistinct == DEFAULT_NUM_DISTINCT)
{
ReleaseVariableStats(vardata);
return (Selectivity) 0.1;
}
stanullfrac = 0.0;
}
/* Compute avg freq of all distinct data values in raw relation */
avgfreq = (1.0 - stanullfrac) / ndistinct;
/*
* Adjust ndistinct to account for restriction clauses. Observe we are
* assuming that the data distribution is affected uniformly by the
* restriction clauses!
*
* XXX Possibly better way, but much more expensive: multiply by
* selectivity of rel's restriction clauses that mention the target Var.
*/
if (vardata.rel)
ndistinct *= vardata.rel->rows / vardata.rel->tuples;
/*
* Initial estimate of bucketsize fraction is 1/nbuckets as long as the
* number of buckets is less than the expected number of distinct values;
* otherwise it is 1/ndistinct.
*/
if (ndistinct > nbuckets)
estfract = 1.0 / nbuckets;
else
estfract = 1.0 / ndistinct;
/*
* Look up the frequency of the most common value, if available.
*/
mcvfreq = 0.0;
if (HeapTupleIsValid(getStatsTuple(&vardata)))
{
HeapTuple tp = getStatsTuple(&vardata);
if (get_attstatsslot(tp,
vardata.atttype, vardata.atttypmod,
STATISTIC_KIND_MCV, InvalidOid,
NULL, NULL, &numbers, &nnumbers))
{
/*
* The first MCV stat is for the most common value.
*/
if (nnumbers > 0)
mcvfreq = numbers[0];
free_attstatsslot(vardata.atttype, NULL, 0,
numbers, nnumbers);
}
}
/*
* Adjust estimated bucketsize upward to account for skewed distribution.
*/
if (avgfreq > 0.0 && mcvfreq > avgfreq)
estfract *= mcvfreq / avgfreq;
/*
* Clamp bucketsize to sane range (the above adjustment could easily
* produce an out-of-range result). We set the lower bound a little above
* zero, since zero isn't a very sane result.
*/
if (estfract < 1.0e-6)
estfract = 1.0e-6;
else if (estfract > 1.0)
estfract = 1.0;
ReleaseVariableStats(vardata);
return (Selectivity) estfract;
}
/*-------------------------------------------------------------------------
*
* Support routines
*
*-------------------------------------------------------------------------
*/
/*
* convert_to_scalar
* Convert non-NULL values of the indicated types to the comparison
* scale needed by scalarineqsel().
* Returns "true" if successful.
*
* XXX this routine is a hack: ideally we should look up the conversion
* subroutines in pg_type.
*
* All numeric datatypes are simply converted to their equivalent
* "double" values. (NUMERIC values that are outside the range of "double"
* are clamped to +/- HUGE_VAL.)
*
* String datatypes are converted to have hi and lo bound be constants, with
* the scaledvalue equally either hi or lo, depending on the value of isgt
* (done so that the caller will include the entire bucket in the final
* computed selectivity, even after inverting for the isgt case)
*
* The bytea datatype is just enough different from strings that it has
* to be treated separately.
*
* The several datatypes representing absolute times are all converted
* to Timestamp, which is actually a double, and then we just use that
* double value. Note this will give correct results even for the "special"
* values of Timestamp, since those are chosen to compare correctly;
* see timestamp_cmp.
*
* The several datatypes representing relative times (intervals) are all
* converted to measurements expressed in seconds.
*
* isgt can be used by datatypes which cannot interpolate and instead must
* return an appropriate default
*
*/
static bool
convert_to_scalar(Datum value, Oid valuetypid, double *scaledvalue,
Datum lobound, Datum hibound, Oid boundstypid,
double *scaledlobound, double *scaledhibound,
bool isgt)
{
/*
* Both the valuetypid and the boundstypid should exactly match the
* declared input type(s) of the operator we are invoked for, so we just
* error out if either is not recognized.
*
* XXX The histogram we are interpolating between points of could belong
* to a column that's only binary-compatible with the declared type. In
* essence we are assuming that the semantics of binary-compatible types
* are enough alike that we can use a histogram generated with one type's
* operators to estimate selectivity for the other's. This is outright
* wrong in some cases --- in particular signed versus unsigned
* interpretation could trip us up. But it's useful enough in the
* majority of cases that we do it anyway. Should think about more
* rigorous ways to do it.
*/
switch (valuetypid)
{
/*
* Built-in numeric types
*/
case BOOLOID:
case INT2OID:
case INT4OID:
case INT8OID:
case FLOAT4OID:
case FLOAT8OID:
case NUMERICOID:
case OIDOID:
case REGPROCOID:
case REGPROCEDUREOID:
case REGOPEROID:
case REGOPERATOROID:
case REGCLASSOID:
case REGTYPEOID:
*scaledvalue = convert_numeric_to_scalar(value, valuetypid);
*scaledlobound = convert_numeric_to_scalar(lobound, boundstypid);
*scaledhibound = convert_numeric_to_scalar(hibound, boundstypid);
return true;
/*
* Built-in string types
*/
case CHAROID:
case BPCHAROID:
case VARCHAROID:
case TEXTOID:
case NAMEOID:
{
*scaledlobound = 1;
*scaledhibound = 2;
*scaledvalue = isgt ? 1 : 2;
return true;
}
/*
* Built-in bytea type
*/
case BYTEAOID:
{
convert_bytea_to_scalar(value, scaledvalue,
lobound, scaledlobound,
hibound, scaledhibound);
return true;
}
/*
* Built-in time types
*/
case TIMESTAMPOID:
case TIMESTAMPTZOID:
case ABSTIMEOID:
case DATEOID:
case INTERVALOID:
case RELTIMEOID:
case TINTERVALOID:
case TIMEOID:
case TIMETZOID:
*scaledvalue = convert_timevalue_to_scalar(value, valuetypid);
*scaledlobound = convert_timevalue_to_scalar(lobound, boundstypid);
*scaledhibound = convert_timevalue_to_scalar(hibound, boundstypid);
return true;
/*
* Built-in network types
*/
case INETOID:
case CIDROID:
case MACADDROID:
*scaledvalue = convert_network_to_scalar(value, valuetypid);
*scaledlobound = convert_network_to_scalar(lobound, boundstypid);
*scaledhibound = convert_network_to_scalar(hibound, boundstypid);
return true;
}
/* Don't know how to convert */
*scaledvalue = *scaledlobound = *scaledhibound = 0;
return false;
}
/*
* Do convert_to_scalar()'s work for any numeric data type.
*/
static double
convert_numeric_to_scalar(Datum value, Oid typid)
{
switch (typid)
{
case BOOLOID:
return (double) DatumGetBool(value);
case INT2OID:
return (double) DatumGetInt16(value);
case INT4OID:
return (double) DatumGetInt32(value);
case INT8OID:
return (double) DatumGetInt64(value);
case FLOAT4OID:
return (double) DatumGetFloat4(value);
case FLOAT8OID:
return (double) DatumGetFloat8(value);
case NUMERICOID:
/* Note: out-of-range values will be clamped to +-HUGE_VAL */
return (double)
DatumGetFloat8(DirectFunctionCall1(numeric_float8_no_overflow,
value));
case OIDOID:
case REGPROCOID:
case REGPROCEDUREOID:
case REGOPEROID:
case REGOPERATOROID:
case REGCLASSOID:
case REGTYPEOID:
/* we can treat OIDs as integers... */
return (double) DatumGetObjectId(value);
}
/*
* Can't get here unless someone tries to use scalarltsel/scalargtsel on
* an operator with one numeric and one non-numeric operand.
*/
elog(ERROR, "unsupported type: %u", typid);
return 0;
}
/*
* Do convert_to_scalar()'s work for any bytea data type.
*
* Very similar to the old convert_string_to_scalar except we can't assume
* null-termination and therefore pass explicit lengths around.
*
* Also, assumptions about likely "normal" ranges of characters have been
* removed - a data range of 0..255 is always used, for now. (Perhaps
* someday we will add information about actual byte data range to
* pg_statistic.)
*/
static void
convert_bytea_to_scalar(Datum value,
double *scaledvalue,
Datum lobound,
double *scaledlobound,
Datum hibound,
double *scaledhibound)
{
int rangelo,
rangehi,
valuelen = VARSIZE(DatumGetPointer(value)) - VARHDRSZ,
loboundlen = VARSIZE(DatumGetPointer(lobound)) - VARHDRSZ,
hiboundlen = VARSIZE(DatumGetPointer(hibound)) - VARHDRSZ,
i,
minlen;
unsigned char *valstr = (unsigned char *) VARDATA(DatumGetPointer(value)),
*lostr = (unsigned char *) VARDATA(DatumGetPointer(lobound)),
*histr = (unsigned char *) VARDATA(DatumGetPointer(hibound));
/*
* Assume bytea data is uniformly distributed across all byte values.
*/
rangelo = 0;
rangehi = 255;
/*
* Now strip any common prefix of the three strings.
*/
minlen = Min(Min(valuelen, loboundlen), hiboundlen);
for (i = 0; i < minlen; i++)
{
if (*lostr != *histr || *lostr != *valstr)
break;
lostr++, histr++, valstr++;
loboundlen--, hiboundlen--, valuelen--;
}
/*
* Now we can do the conversions.
*/
*scaledvalue = convert_one_bytea_to_scalar(valstr, valuelen, rangelo, rangehi);
*scaledlobound = convert_one_bytea_to_scalar(lostr, loboundlen, rangelo, rangehi);
*scaledhibound = convert_one_bytea_to_scalar(histr, hiboundlen, rangelo, rangehi);
}
static double
convert_one_bytea_to_scalar(unsigned char *value, int valuelen,
int rangelo, int rangehi)
{
double num,
denom,
base;
if (valuelen <= 0)
return 0.0; /* empty string has scalar value 0 */
/*
* Since base is 256, need not consider more than about 10 chars (even
* this many seems like overkill)
*/
if (valuelen > 10)
valuelen = 10;
/* Convert initial characters to fraction */
base = rangehi - rangelo + 1;
num = 0.0;
denom = base;
while (valuelen-- > 0)
{
int ch = *value++;
if (ch < rangelo)
ch = rangelo - 1;
else if (ch > rangehi)
ch = rangehi + 1;
num += ((double) (ch - rangelo)) / denom;
denom *= base;
}
return num;
}
/*
* Do convert_to_scalar()'s work for any timevalue data type.
*/
double
convert_timevalue_to_scalar(Datum value, Oid typid)
{
switch (typid)
{
case TIMESTAMPOID:
return DatumGetTimestamp(value);
case TIMESTAMPTZOID:
return DatumGetTimestampTz(value);
case ABSTIMEOID:
return DatumGetTimestamp(DirectFunctionCall1(abstime_timestamp,
value));
case DATEOID:
return DatumGetTimestamp(DirectFunctionCall1(date_timestamp,
value));
case INTERVALOID:
{
Interval *interval = DatumGetIntervalP(value);
/*
* Convert the month part of Interval to days using assumed
* average month length of 365.25/12.0 days. Not too
* accurate, but plenty good enough for our purposes.
*/
#ifdef HAVE_INT64_TIMESTAMP
return interval->time + interval->day * (double) USECS_PER_DAY +
interval->month * ((DAYS_PER_YEAR / (double) MONTHS_PER_YEAR) * USECS_PER_DAY);
#else
return interval->time + interval->day * SECS_PER_DAY +
interval->month * ((DAYS_PER_YEAR / (double) MONTHS_PER_YEAR) * (double) SECS_PER_DAY);
#endif
}
case RELTIMEOID:
#ifdef HAVE_INT64_TIMESTAMP
return (DatumGetRelativeTime(value) * 1000000.0);
#else
return DatumGetRelativeTime(value);
#endif
case TINTERVALOID:
{
TimeInterval tinterval = DatumGetTimeInterval(value);
#ifdef HAVE_INT64_TIMESTAMP
if (tinterval->status != 0)
return ((tinterval->data[1] - tinterval->data[0]) * 1000000.0);
#else
if (tinterval->status != 0)
return tinterval->data[1] - tinterval->data[0];
#endif
return 0; /* for lack of a better idea */
}
case TIMEOID:
return DatumGetTimeADT(value);
case TIMETZOID:
{
TimeTzADT *timetz = DatumGetTimeTzADTP(value);
/* use GMT-equivalent time */
#ifdef HAVE_INT64_TIMESTAMP
return (double) (timetz->time + (timetz->zone * 1000000.0));
#else
return (double) (timetz->time + timetz->zone);
#endif
}
}
/*
* Can't get here unless someone tries to use scalarltsel/scalargtsel on
* an operator with one timevalue and one non-timevalue operand.
*/
elog(ERROR, "unsupported type: %u", typid);
return 0;
}
/*
* get_restriction_variable
* Examine the args of a restriction clause to see if it's of the
* form (variable op pseudoconstant) or (pseudoconstant op variable),
* where "variable" could be either a Var or an expression in vars of a
* single relation. If so, extract information about the variable,
* and also indicate which side it was on and the other argument.
*
* Inputs:
* root: the planner info
* args: clause argument list
* varRelid: see specs for restriction selectivity functions
*
* Outputs: (these are valid only if TRUE is returned)
* *vardata: gets information about variable (see examine_variable)
* *other: gets other clause argument, aggressively reduced to a constant
* *varonleft: set TRUE if variable is on the left, FALSE if on the right
*
* Returns TRUE if a variable is identified, otherwise FALSE.
*
* Note: if there are Vars on both sides of the clause, we must fail, because
* callers are expecting that the other side will act like a pseudoconstant.
*/
bool
get_restriction_variable(PlannerInfo *root, List *args, int varRelid,
VariableStatData *vardata, Node **other,
bool *varonleft)
{
Node *left,
*right;
VariableStatData rdata;
/* Fail if not a binary opclause (probably shouldn't happen) */
if (list_length(args) != 2)
return false;
left = (Node *) linitial(args);
right = (Node *) lsecond(args);
/*
* Examine both sides. Note that when varRelid is nonzero, Vars of other
* relations will be treated as pseudoconstants.
*/
examine_variable(root, left, varRelid, vardata);
examine_variable(root, right, varRelid, &rdata);
/*
* If one side is a variable and the other not, we win.
*/
if (vardata->rel && rdata.rel == NULL)
{
*varonleft = true;
*other = estimate_expression_value(root, rdata.var);
/* Assume we need no ReleaseVariableStats(rdata) here */
return true;
}
if (vardata->rel == NULL && rdata.rel)
{
*varonleft = false;
*other = estimate_expression_value(root, vardata->var);
/* Assume we need no ReleaseVariableStats(*vardata) here */
*vardata = rdata;
return true;
}
/* Ooops, clause has wrong structure (probably var op var) */
ReleaseVariableStats(*vardata);
ReleaseVariableStats(rdata);
return false;
}
/*
* get_join_variables
* Apply examine_variable() to each side of a join clause.
*/
void
get_join_variables(PlannerInfo *root, List *args,
VariableStatData *vardata1, VariableStatData *vardata2)
{
Node *left,
*right;
if (list_length(args) != 2)
elog(ERROR, "join operator should take two arguments");
left = (Node *) linitial(args);
right = (Node *) lsecond(args);
examine_variable(root, left, 0, vardata1);
examine_variable(root, right, 0, vardata2);
}
/*
* This method returns a pointer to the largest child relation for an inherited (incl partitioned)
* relation. If there are multiple levels in the hierarchy, we delve down recursively till we
* find the largest (as determined from the path structure).
* Input: a partitioned table
* Output: largest child partition. If there are no child partition because all of them have been eliminated, then
* returns NULL.
*/
static RelOptInfo* largest_child_relation(PlannerInfo *root, RelOptInfo *rel)
{
AppendPath *append_path = NULL;
ListCell *subpath_lc = NULL;
RelOptInfo *largest_child_in_subpath = NULL;
double max_rows = -1.0;
Assert(IsA(rel->cheapest_total_path, AppendPath));
append_path = (AppendPath *) rel->cheapest_total_path;
foreach(subpath_lc, append_path->subpaths)
{
RelOptInfo *candidate_child = NULL;
Path *subpath = lfirst(subpath_lc);
if (IsA(subpath, AppendPath))
{
candidate_child = largest_child_relation(root, subpath->parent);
}
else
{
candidate_child = subpath->parent;
}
if (candidate_child && candidate_child->rows > max_rows)
{
max_rows = candidate_child->rows;
largest_child_in_subpath = candidate_child;
}
}
return largest_child_in_subpath;
}
/*
* The purpose of this method is to make the statistics (on a specific column) of a child partition
* representative of the parent relation. This entails the following assumptions:
* 1. if ndistinct<=-1.0 in child partition, the column is a unique column in the child partition. We
* expect the column to remain distinct in the master as well.
* 2. if -1.0 < ndistinct < 0.0, the absolute number of ndistinct values in the child partition is a fraction
* of the number of rows in the partition. We expect that the absolute number of ndistinct in the master
* to stay the same. Therefore, we convert this to a positive number.
* The method get_variable_numdistinct will multiply this by the number of tuples in the master relation.
* 3. if ndistinct is positive, it indicates a small absolute number of distinct values. We expect these
* values to be repeated in all partitions. Therefore, we expect no change in the ndistinct in the master.
*
* Input:
* statsTuple, which is a heaptuple representing statistics on a child relation. It expects statstuple to be non-null.
* scalefactor, which is in the range (0.0,1.0]
*
* Output:
* This method modifies the tuple passed to it.
*/
static void inline adjust_partition_table_statistic_for_parent(HeapTuple statsTuple, double childtuples)
{
Form_pg_statistic stats;
Assert(HeapTupleIsValid(statsTuple));
stats = (Form_pg_statistic) GETSTRUCT(statsTuple);
if (stats->stadistinct <= -1.0)
{
/*
* Case 1 as described above.
*/
return;
}
else if (stats->stadistinct < 0.0)
{
/*
* Case 2 as described above.
*/
stats->stadistinct = ((double) -1.0) * stats->stadistinct * childtuples;
}
else
{
/**
* Case 3 as described above.
*/
return;
}
}
/*
* examine_variable
* Try to look up statistical data about an expression.
* Fill in a VariableStatData struct to describe the expression.
*
* Inputs:
* root: the planner info
* node: the expression tree to examine
* varRelid: see specs for restriction selectivity functions
*
* Outputs: *vardata is filled as follows:
* var: the input expression (with any binary relabeling stripped, if
* it is or contains a variable; but otherwise the type is preserved)
* rel: RelOptInfo for relation containing variable; NULL if expression
* contains no Vars (NOTE this could point to a RelOptInfo of a
* subquery, not one in the current query).
* statsTuple: the pg_statistic entry for the variable, if one exists;
* otherwise NULL.
* vartype: exposed type of the expression; this should always match
* the declared input type of the operator we are estimating for.
* atttype, atttypmod: type data to pass to get_attstatsslot(). This is
* commonly the same as the exposed type of the variable argument,
* but can be different in binary-compatible-type cases.
*
* Caller is responsible for doing ReleaseVariableStats() before exiting.
*/
void
examine_variable(PlannerInfo *root, Node *node, int varRelid,
VariableStatData *vardata)
{
Node *basenode;
Relids varnos;
RelOptInfo *onerel;
/* Make sure we don't return dangling pointers in vardata */
MemSet(vardata, 0, sizeof(VariableStatData));
/* Save the exposed type of the expression */
vardata->vartype = exprType(node);
vardata->numdistinctFromPrimaryKey = -1.0; /* ignore by default*/
/* Look inside any binary-compatible relabeling */
if (IsA(node, RelabelType))
basenode = (Node *) ((RelabelType *) node)->arg;
else
basenode = node;
/* Fast path for a simple Var */
if (IsA(basenode, Var) &&
(varRelid == 0 || varRelid == ((Var *) basenode)->varno))
{
Var *var = (Var *) basenode;
RangeTblEntry *rte;
vardata->var = basenode; /* return Var without relabeling */
vardata->rel = find_base_rel(root, var->varno);
vardata->atttype = var->vartype;
vardata->atttypmod = var->vartypmod;
rte = rt_fetch(var->varno, root->parse->rtable);
/*
* If this attribute has a foreign key relationship, then first look
* at primary key statistics. If there exist stats on that attribute,
* we utilize those. If not, continue.
*/
if (gp_statistics_use_fkeys)
{
Oid pkrelid = InvalidOid;
AttrNumber pkattno = -1;
if (ConstraintGetPrimaryKeyOf(rte->relid, var->varattno, &pkrelid, &pkattno))
{
cqContext *relcqCtx;
HeapTuple pkStatsTuple;
/* SELECT reltuples FROM pg_class */
relcqCtx = caql_beginscan(
NULL,
cql("SELECT * FROM pg_class "
" WHERE oid = :1 ",
ObjectIdGetDatum(pkrelid)));
pkStatsTuple = caql_getnext(relcqCtx);
if (HeapTupleIsValid(pkStatsTuple))
{
Form_pg_class classForm = (Form_pg_class) GETSTRUCT(pkStatsTuple);
if (classForm->reltuples > 0)
{
vardata->numdistinctFromPrimaryKey = classForm->reltuples;
}
}
caql_endscan(relcqCtx);
}
}
if (rte->inh)
{
/*
* If gp_statistics_pullup_from_child_partition is set, we attempt to pull up statistics from
* the largest child partition in an inherited or a partitioned table.
*/
if (gp_statistics_pullup_from_child_partition &&
vardata->rel->cheapest_total_path != NULL)
{
RelOptInfo *childrel = largest_child_relation(root, vardata->rel);
vardata->statscqCtx = NULL;
if (childrel)
{
RangeTblEntry *child_rte = NULL;
child_rte = rt_fetch(childrel->relid, root->parse->rtable);
Assert(child_rte != NULL);
/*
* Get statistics from the child partition.
*/
vardata->statscqCtx = caql_beginscan
(
NULL,
cql("SELECT * FROM pg_statistic "
" WHERE starelid = :1 "
" AND staattnum = :2 ",
ObjectIdGetDatum(child_rte->relid),
Int16GetDatum(var->varattno))
);
(void) caql_getnext(vardata->statscqCtx);
if (NULL != vardata->statscqCtx && NULL != vardata->statscqCtx->cq_lasttup)
{
adjust_partition_table_statistic_for_parent(vardata->statscqCtx->cq_lasttup, childrel->tuples);
}
}
}
}
else if (rte->rtekind == RTE_RELATION)
{
vardata->statscqCtx = caql_beginscan(
NULL,
cql("SELECT * FROM pg_statistic "
" WHERE starelid = :1 "
" AND staattnum = :2 ",
ObjectIdGetDatum(rte->relid),
Int16GetDatum(var->varattno)));
/* fetch the tuple */
(void) caql_getnext(vardata->statscqCtx);
}
else
{
/*
* XXX This means the Var comes from a JOIN or sub-SELECT. Later
* add code to dig down into the join etc and see if we can trace
* the variable to something with stats. (But beware of
* sub-SELECTs with DISTINCT/GROUP BY/etc. Perhaps there are no
* cases where this would really be useful, because we'd have
* flattened the subselect if it is??)
*/
}
return;
}
/*
* Okay, it's a more complicated expression. Determine variable
* membership. Note that when varRelid isn't zero, only vars of that
* relation are considered "real" vars.
*/
varnos = pull_varnos(basenode);
onerel = NULL;
switch (bms_membership(varnos))
{
case BMS_EMPTY_SET:
/* No Vars at all ... must be pseudo-constant clause */
break;
case BMS_SINGLETON:
if (varRelid == 0 || bms_is_member(varRelid, varnos))
{
onerel = find_base_rel(root,
(varRelid ? varRelid : bms_singleton_member(varnos)));
vardata->rel = onerel;
node = basenode; /* strip any relabeling */
}
/* else treat it as a constant */
break;
case BMS_MULTIPLE:
if (varRelid == 0)
{
/* treat it as a variable of a join relation */
vardata->rel = find_join_rel(root, varnos);
node = basenode; /* strip any relabeling */
}
else if (bms_is_member(varRelid, varnos))
{
/* ignore the vars belonging to other relations */
vardata->rel = find_base_rel(root, varRelid);
node = basenode; /* strip any relabeling */
/* note: no point in expressional-index search here */
}
/* else treat it as a constant */
break;
}
bms_free(varnos);
vardata->var = node;
vardata->atttype = exprType(node);
vardata->atttypmod = exprTypmod(node);
if (onerel)
{
/*
* We have an expression in vars of a single relation. Try to match
* it to expressional index columns, in hopes of finding some
* statistics.
*
* XXX it's conceivable that there are multiple matches with different
* index opclasses; if so, we need to pick one that matches the
* operator we are estimating for. FIXME later.
*/
ListCell *ilist;
foreach(ilist, onerel->indexlist)
{
IndexOptInfo *index = (IndexOptInfo *) lfirst(ilist);
ListCell *indexpr_item;
int pos;
indexpr_item = list_head(index->indexprs);
if (indexpr_item == NULL)
continue; /* no expressions here... */
/*
* Ignore partial indexes since they probably don't reflect
* whole-relation statistics. Possibly reconsider this later.
*/
if (index->indpred)
continue;
for (pos = 0; pos < index->ncolumns; pos++)
{
if (index->indexkeys[pos] == 0)
{
Node *indexkey;
if (indexpr_item == NULL)
elog(ERROR, "too few entries in indexprs list");
indexkey = (Node *) lfirst(indexpr_item);
if (indexkey && IsA(indexkey, RelabelType))
indexkey = (Node *) ((RelabelType *) indexkey)->arg;
if (equal(node, indexkey))
{
/*
* Found a match ... is it a unique index? Tests here
* should match has_unique_index().
*/
if (index->unique &&
index->ncolumns == 1 &&
index->indpred == NIL)
vardata->isunique = true;
/* Has it got stats? */
vardata->statscqCtx = caql_beginscan(
NULL,
cql("SELECT * FROM pg_statistic "
" WHERE starelid = :1 "
" AND staattnum = :2 ",
ObjectIdGetDatum(index->indexoid),
Int16GetDatum(pos + 1)));
/* fetch the tuple */
(void) caql_getnext(vardata->statscqCtx);
if (HeapTupleIsValid(getStatsTuple(vardata)))
break;
}
indexpr_item = lnext(indexpr_item);
}
}
if (HeapTupleIsValid(getStatsTuple(vardata)))
break;
}
}
}
/**
* Input: vardata. Must be non-NULL.
*/
void ReleaseVariableStats(VariableStatData vardata)
{
if (vardata.statscqCtx)
caql_endscan(vardata.statscqCtx);
vardata.statscqCtx = NULL;
}
/*
* get_variable_numdistinct
* Estimate the number of distinct values of a variable.
*
* vardata: results of examine_variable
*
* NB: be careful to produce an integral result, since callers may compare
* the result to exact integer counts.
*/
double
get_variable_numdistinct(VariableStatData *vardata)
{
double stadistinct;
double ntuples;
/**
* If we have an estimate from the primary key, then that is the most accurate value.
*/
if (gp_statistics_use_fkeys &&
vardata->numdistinctFromPrimaryKey > 0.0)
{
return vardata->numdistinctFromPrimaryKey;
}
/*
* Determine the stadistinct value to use. There are cases where we can
* get an estimate even without a pg_statistic entry, or can get a better
* value than is in pg_statistic.
*/
if (HeapTupleIsValid(getStatsTuple(vardata)))
{
/* Use the pg_statistic entry */
Form_pg_statistic stats;
HeapTuple tp = getStatsTuple(vardata);
stats = (Form_pg_statistic) GETSTRUCT(tp);
stadistinct = stats->stadistinct;
}
else if (vardata->vartype == BOOLOID)
{
/*
* Special-case boolean columns: presumably, two distinct values.
*
* Are there any other datatypes we should wire in special estimates
* for?
*/
stadistinct = 2.0;
}
else
{
/*
* We don't keep statistics for system columns, but in some cases we
* can infer distinctness anyway.
*/
if (vardata->var && IsA(vardata->var, Var))
{
switch (((Var *) vardata->var)->varattno)
{
case ObjectIdAttributeNumber:
case SelfItemPointerAttributeNumber:
stadistinct = -1.0; /* unique */
break;
case TableOidAttributeNumber:
stadistinct = 1.0; /* only 1 value */
break;
case GpSegmentIdAttributeNumber: /*CDB*/
stadistinct = GetPlannerSegmentNum();
break;
default:
stadistinct = 0.0; /* means "unknown" */
break;
}
}
else
stadistinct = 0.0; /* means "unknown" */
/*
* XXX consider using estimate_num_groups on expressions?
*/
}
/*
* If there is a unique index for the variable, assume it is unique no
* matter what pg_statistic says (the statistics could be out of date).
* Can skip search if we already think it's unique.
*/
if (stadistinct != -1.0)
{
if (vardata->isunique)
stadistinct = -1.0;
else if (vardata->var && IsA(vardata->var, Var) &&
vardata->rel &&
has_unique_index(vardata->rel,
((Var *) vardata->var)->varattno))
stadistinct = -1.0;
}
/*
* If we had an absolute estimate, use that.
*/
if (stadistinct > 0.0)
return stadistinct;
/*
* Otherwise we need to get the relation size; punt if not available.
*/
if (vardata->rel == NULL)
return DEFAULT_NUM_DISTINCT;
ntuples = vardata->rel->tuples;
if (ntuples <= 0.0)
return DEFAULT_NUM_DISTINCT;
/*
* If we had a relative estimate, use that.
*/
if (stadistinct < 0.0)
return floor((-stadistinct * ntuples) + 0.5);
/*
* With no data, estimate ndistinct = ntuples if the table is small, else
* use default.
*/
if (ntuples < DEFAULT_NUM_DISTINCT)
return ntuples;
return DEFAULT_NUM_DISTINCT;
}
/*
* get_variable_maximum
* Estimate the maximum value of the specified variable.
* If successful, store value in *max and return TRUE.
* If no data available, return FALSE.
*
* sortop is the "<" comparison operator to use. (To extract the
* minimum instead of the maximum, just pass the ">" operator instead.)
*/
static bool
get_variable_maximum(PlannerInfo *root, VariableStatData *vardata,
Oid sortop, Datum *max)
{
Datum tmax = 0;
bool have_max = false;
Form_pg_statistic stats;
int16 typLen;
bool typByVal;
Datum *values;
int nvalues;
int i;
HeapTuple tp = getStatsTuple(vardata);
if (!HeapTupleIsValid(tp))
{
/* no stats available, so default result */
return false;
}
stats = (Form_pg_statistic) GETSTRUCT(tp);
get_typlenbyval(vardata->atttype, &typLen, &typByVal);
/*
* If there is a histogram, grab the last or first value as appropriate.
*
* If there is a histogram that is sorted with some other operator than
* the one we want, fail --- this suggests that there is data we can't
* use.
*/
if (get_attstatsslot(tp,
vardata->atttype, vardata->atttypmod,
STATISTIC_KIND_HISTOGRAM, sortop,
&values, &nvalues,
NULL, NULL))
{
if (nvalues > 0)
{
tmax = datumCopy(values[nvalues - 1], typByVal, typLen);
have_max = true;
}
free_attstatsslot(vardata->atttype, values, nvalues, NULL, 0);
}
else
{
Oid rsortop = get_commutator(sortop);
if (OidIsValid(rsortop) &&
get_attstatsslot(tp,
vardata->atttype, vardata->atttypmod,
STATISTIC_KIND_HISTOGRAM, rsortop,
&values, &nvalues,
NULL, NULL))
{
if (nvalues > 0)
{
tmax = datumCopy(values[0], typByVal, typLen);
have_max = true;
}
free_attstatsslot(vardata->atttype, values, nvalues, NULL, 0);
}
else if (get_attstatsslot(tp,
vardata->atttype, vardata->atttypmod,
STATISTIC_KIND_HISTOGRAM, InvalidOid,
&values, &nvalues,
NULL, NULL))
{
free_attstatsslot(vardata->atttype, values, nvalues, NULL, 0);
return false;
}
}
/*
* If we have most-common-values info, look for a large MCV. This is
* needed even if we also have a histogram, since the histogram excludes
* the MCVs. However, usually the MCVs will not be the extreme values, so
* avoid unnecessary data copying.
*/
if (get_attstatsslot(tp,
vardata->atttype, vardata->atttypmod,
STATISTIC_KIND_MCV, InvalidOid,
&values, &nvalues,
NULL, NULL))
{
bool large_mcv = false;
FmgrInfo opproc;
fmgr_info(get_opcode(sortop), &opproc);
for (i = 0; i < nvalues; i++)
{
if (!have_max)
{
tmax = values[i];
large_mcv = have_max = true;
}
else if (DatumGetBool(FunctionCall2(&opproc, tmax, values[i])))
{
tmax = values[i];
large_mcv = true;
}
}
if (large_mcv)
tmax = datumCopy(tmax, typByVal, typLen);
free_attstatsslot(vardata->atttype, values, nvalues, NULL, 0);
}
*max = tmax;
return have_max;
}
/*-------------------------------------------------------------------------
*
* Pattern analysis functions
*
* These routines support analysis of LIKE and regular-expression patterns
* by the planner/optimizer. It's important that they agree with the
* regular-expression code in backend/regex/ and the LIKE code in
* backend/utils/adt/like.c. Also, the computation of the fixed prefix
* must be conservative: if we report a string longer than the true fixed
* prefix, the query may produce actually wrong answers, rather than just
* getting a bad selectivity estimate!
*
* Note that the prefix-analysis functions are called from
* backend/optimizer/path/indxpath.c as well as from routines in this file.
*
*-------------------------------------------------------------------------
*/
/*
* Extract the fixed prefix, if any, for a pattern.
*
* *prefix is set to a palloc'd prefix string (in the form of a Const node),
* or to NULL if no fixed prefix exists for the pattern.
* *rest is set to a palloc'd Const representing the remainder of the pattern
* after the portion describing the fixed prefix.
* Each of these has the same type (TEXT or BYTEA) as the given pattern Const.
*
* The return value distinguishes no fixed prefix, a partial prefix,
* or an exact-match-only pattern.
*/
static Pattern_Prefix_Status
like_fixed_prefix(Const *patt_const, bool case_insensitive,
Const **prefix_const, Const **rest_const)
{
char *match;
char *patt;
int pattlen;
char *rest;
Oid typeid = patt_const->consttype;
int pos,
match_pos;
bool is_multibyte = (pg_database_encoding_max_length() > 1);
/* the right-hand const is type text or bytea */
Assert(typeid == BYTEAOID || typeid == TEXTOID);
if (typeid == BYTEAOID && case_insensitive)
ereport(ERROR,
(errcode(ERRCODE_FEATURE_NOT_SUPPORTED),
errmsg("case insensitive matching not supported on type bytea")));
if (typeid != BYTEAOID)
{
patt = DatumGetCString(DirectFunctionCall1(textout, patt_const->constvalue));
pattlen = strlen(patt);
}
else
{
bytea *bstr = DatumGetByteaP(patt_const->constvalue);
pattlen = VARSIZE(bstr) - VARHDRSZ;
patt = (char *) palloc(pattlen);
memcpy(patt, VARDATA(bstr), pattlen);
if ((Pointer) bstr != DatumGetPointer(patt_const->constvalue))
pfree(bstr);
}
match = palloc(pattlen + 1);
match_pos = 0;
for (pos = 0; pos < pattlen; pos++)
{
/* % and _ are wildcard characters in LIKE */
if (patt[pos] == '%' ||
patt[pos] == '_')
break;
/* Backslash escapes the next character */
if (patt[pos] == '\\')
{
pos++;
if (pos >= pattlen)
break;
}
/*
* XXX In multibyte character sets, we can't trust isalpha, so assume
* any multibyte char is potentially case-varying.
*/
if (case_insensitive)
{
if (is_multibyte && (unsigned char) patt[pos] >= 0x80)
break;
if (isalpha((unsigned char) patt[pos]))
break;
}
/*
* NOTE: this code used to think that %% meant a literal %, but
* textlike() itself does not think that, and the SQL92 spec doesn't
* say any such thing either.
*/
match[match_pos++] = patt[pos];
}
match[match_pos] = '\0';
rest = &patt[pos];
if (typeid != BYTEAOID)
{
*prefix_const = string_to_const(match, typeid);
*rest_const = string_to_const(rest, typeid);
}
else
{
*prefix_const = string_to_bytea_const(match, match_pos);
*rest_const = string_to_bytea_const(rest, pattlen - pos);
}
pfree(patt);
pfree(match);
/* in LIKE, an empty pattern is an exact match! */
if (pos == pattlen)
return Pattern_Prefix_Exact; /* reached end of pattern, so exact */
if (match_pos > 0)
return Pattern_Prefix_Partial;
return Pattern_Prefix_None;
}
static Pattern_Prefix_Status
regex_fixed_prefix(Const *patt_const, bool case_insensitive,
Const **prefix_const, Const **rest_const)
{
char *match;
int pos,
match_pos,
prev_pos,
prev_match_pos;
bool have_leading_paren;
char *patt;
char *rest;
Oid typeid = patt_const->consttype;
bool is_basic = regex_flavor_is_basic();
bool is_multibyte = (pg_database_encoding_max_length() > 1);
/*
* Should be unnecessary, there are no bytea regex operators defined. As
* such, it should be noted that the rest of this function has *not* been
* made safe for binary (possibly NULL containing) strings.
*/
if (typeid == BYTEAOID)
ereport(ERROR,
(errcode(ERRCODE_FEATURE_NOT_SUPPORTED),
errmsg("regular-expression matching not supported on type bytea")));
/* the right-hand const is type text for all of these */
patt = DatumGetCString(DirectFunctionCall1(textout, patt_const->constvalue));
/*
* Check for ARE director prefix. It's worth our trouble to recognize
* this because similar_escape() uses it.
*/
pos = 0;
if (strncmp(patt, "***:", 4) == 0)
{
pos = 4;
is_basic = false;
}
/* Pattern must be anchored left */
if (patt[pos] != '^')
{
rest = patt;
*prefix_const = NULL;
*rest_const = string_to_const(rest, typeid);
return Pattern_Prefix_None;
}
pos++;
/*
* If '|' is present in pattern, then there may be multiple alternatives
* for the start of the string. (There are cases where this isn't so,
* for instance if the '|' is inside parens, but detecting that reliably
* is too hard.)
*/
if (strchr(patt + pos, '|') != NULL)
{
rest = patt;
*prefix_const = NULL;
*rest_const = string_to_const(rest, typeid);
return Pattern_Prefix_None;
}
/* OK, allocate space for pattern */
match = palloc(strlen(patt) + 1);
prev_match_pos = match_pos = 0;
/*
* We special-case the syntax '^(...)$' because psql uses it. But beware:
* in BRE mode these parentheses are just ordinary characters. Also,
* sequences beginning "(?" are not what they seem, unless they're "(?:".
* (We should recognize that, too, because of similar_escape().)
*
* Note: it's a bit bogus to be depending on the current regex_flavor
* setting here, because the setting could change before the pattern is
* used. We minimize the risk by trusting the flavor as little as we can,
* but perhaps it would be a good idea to get rid of the "basic" setting.
*/
have_leading_paren = false;
if (patt[pos] == '(' && !is_basic &&
(patt[pos + 1] != '?' || patt[pos + 2] == ':'))
{
have_leading_paren = true;
pos += (patt[pos + 1] != '?' ? 1 : 3);
}
/* Scan remainder of pattern */
prev_pos = pos;
while (patt[pos])
{
int len;
/*
* Check for characters that indicate multiple possible matches here.
* Also, drop out at ')' or '$' so the termination test works right.
*/
if (patt[pos] == '.' ||
patt[pos] == '(' ||
patt[pos] == ')' ||
patt[pos] == '[' ||
patt[pos] == '^' ||
patt[pos] == '$')
break;
/*
* XXX In multibyte character sets, we can't trust isalpha, so assume
* any multibyte char is potentially case-varying.
*/
if (case_insensitive)
{
if (is_multibyte && (unsigned char) patt[pos] >= 0x80)
break;
if (isalpha((unsigned char) patt[pos]))
break;
}
/*
* Check for quantifiers. Except for +, this means the preceding
* character is optional, so we must remove it from the prefix too!
* Note: in BREs, \{ is a quantifier.
*/
if (patt[pos] == '*' ||
patt[pos] == '?' ||
patt[pos] == '{' ||
(patt[pos] == '\\' && patt[pos + 1] == '{'))
{
match_pos = prev_match_pos;
pos = prev_pos;
break;
}
if (patt[pos] == '+')
{
pos = prev_pos;
break;
}
/*
* Normally, backslash quotes the next character. But in AREs,
* backslash followed by alphanumeric is an escape, not a quoted
* character. Must treat it as having multiple possible matches.
* In BREs, \( is a parenthesis, so don't trust that either.
* Note: since only ASCII alphanumerics are escapes, we don't have
* to be paranoid about multibyte here.
*/
if (patt[pos] == '\\')
{
if (isalnum((unsigned char) patt[pos + 1]) || patt[pos + 1] == '(')
break;
pos++;
if (patt[pos] == '\0')
break;
}
/* save position in case we need to back up on next loop cycle */
prev_match_pos = match_pos;
prev_pos = pos;
/* must use encoding-aware processing here */
len = pg_mblen(&patt[pos]);
memcpy(&match[match_pos], &patt[pos], len);
match_pos += len;
pos += len;
}
match[match_pos] = '\0';
rest = &patt[pos];
if (have_leading_paren && patt[pos] == ')')
pos++;
if (patt[pos] == '$' && patt[pos + 1] == '\0')
{
rest = &patt[pos + 1];
*prefix_const = string_to_const(match, typeid);
*rest_const = string_to_const(rest, typeid);
pfree(patt);
pfree(match);
return Pattern_Prefix_Exact; /* pattern specifies exact match */
}
*prefix_const = string_to_const(match, typeid);
*rest_const = string_to_const(rest, typeid);
pfree(patt);
pfree(match);
if (match_pos > 0)
return Pattern_Prefix_Partial;
return Pattern_Prefix_None;
}
Pattern_Prefix_Status
pattern_fixed_prefix(Const *patt, Pattern_Type ptype,
Const **prefix, Const **rest)
{
Pattern_Prefix_Status result;
switch (ptype)
{
case Pattern_Type_Like:
result = like_fixed_prefix(patt, false, prefix, rest);
break;
case Pattern_Type_Like_IC:
result = like_fixed_prefix(patt, true, prefix, rest);
break;
case Pattern_Type_Regex:
result = regex_fixed_prefix(patt, false, prefix, rest);
break;
case Pattern_Type_Regex_IC:
result = regex_fixed_prefix(patt, true, prefix, rest);
break;
default:
elog(ERROR, "unrecognized ptype: %d", (int) ptype);
result = Pattern_Prefix_None; /* keep compiler quiet */
break;
}
return result;
}
/*
* Estimate the selectivity of a fixed prefix for a pattern match.
*
* A fixed prefix "foo" is estimated as the selectivity of the expression
* "variable >= 'foo' AND variable < 'fop'" (see also indxpath.c).
*
* The selectivity estimate is with respect to the portion of the column
* population represented by the histogram --- the caller must fold this
* together with info about MCVs and NULLs.
*
* We use the >= and < operators from the specified btree opclass to do the
* estimation. The given variable and Const must be of the associated
* datatype.
*
* XXX Note: we make use of the upper bound to estimate operator selectivity
* even if the locale is such that we cannot rely on the upper-bound string.
* The selectivity only needs to be approximately right anyway, so it seems
* more useful to use the upper-bound code than not.
*/
static Selectivity
prefix_selectivity(VariableStatData *vardata, Oid opclass, Const *prefixcon)
{
Selectivity prefixsel;
Oid cmpopr;
FmgrInfo opproc;
Const *greaterstrcon;
cmpopr = get_opclass_member(opclass, InvalidOid,
BTGreaterEqualStrategyNumber);
if (cmpopr == InvalidOid)
elog(ERROR, "no >= operator for opclass %u", opclass);
fmgr_info(get_opcode(cmpopr), &opproc);
prefixsel = ineq_histogram_selectivity(vardata, &opproc, true,
prefixcon->constvalue,
prefixcon->consttype);
if (prefixsel <= 0.0)
{
/* No histogram is present ... return a suitable default estimate */
return 0.005;
}
/*-------
* If we can create a string larger than the prefix, say
* "x < greaterstr".
*-------
*/
cmpopr = get_opclass_member(opclass, InvalidOid,
BTLessStrategyNumber);
if (cmpopr == InvalidOid)
elog(ERROR, "no < operator for opclass %u", opclass);
fmgr_info(get_opcode(cmpopr), &opproc);
greaterstrcon = make_greater_string(prefixcon, &opproc);
if (greaterstrcon)
{
Selectivity topsel;
topsel = ineq_histogram_selectivity(vardata, &opproc, false,
greaterstrcon->constvalue,
greaterstrcon->consttype);
/* ineq_histogram_selectivity worked before, it shouldn't fail now */
Assert(topsel > 0.0);
/*
* Merge the two selectivities in the same way as for a range query
* (see clauselist_selectivity()). Note that we don't need to worry
* about double-exclusion of nulls, since ineq_histogram_selectivity
* doesn't count those anyway.
*/
prefixsel = topsel + prefixsel - 1.0;
/*
* A zero or negative prefixsel should be converted into a small
* positive value; we probably are dealing with a very tight range and
* got a bogus result due to roundoff errors.
*/
if (prefixsel <= 0.0)
prefixsel = 1.0e-10;
}
return prefixsel;
}
/*
* Estimate the selectivity of a pattern of the specified type.
* Note that any fixed prefix of the pattern will have been removed already.
*
* For now, we use a very simplistic approach: fixed characters reduce the
* selectivity a good deal, character ranges reduce it a little,
* wildcards (such as % for LIKE or .* for regex) increase it.
*
* CDB: There is a gradual lessening of the change in selectivity as more
* fixed characters or character ranges are added.
*/
#define FIXED_CHAR_SEL 0.20 /* about 1/5 */
#define CHAR_RANGE_SEL 0.25
#define ANY_CHAR_SEL 0.99 /* not 1, since it won't match end-of-string */
#define CDB_RANCHOR_SEL 0.95
#define CDB_ROLLOFF_SEL 0.14
static Selectivity
like_selectivity(Const *patt_const, bool case_insensitive)
{
Selectivity sel = 1.0;
Selectivity fixed_char_sel = FIXED_CHAR_SEL;
int pos;
Oid typeid = patt_const->consttype;
char *patt;
int pattlen;
/* the right-hand const is type text or bytea */
Assert(typeid == BYTEAOID || typeid == TEXTOID);
if (typeid == BYTEAOID && case_insensitive)
ereport(ERROR,
(errcode(ERRCODE_FEATURE_NOT_SUPPORTED),
errmsg("case insensitive matching not supported on type bytea")));
if (typeid != BYTEAOID)
{
patt = DatumGetCString(DirectFunctionCall1(textout, patt_const->constvalue));
pattlen = strlen(patt);
}
else
{
bytea *bstr = DatumGetByteaP(patt_const->constvalue);
pattlen = VARSIZE(bstr) - VARHDRSZ;
patt = (char *) palloc(pattlen);
memcpy(patt, VARDATA(bstr), pattlen);
if ((Pointer) bstr != DatumGetPointer(patt_const->constvalue))
pfree(bstr);
}
/* Skip any leading %; it's already factored into initial sel */
for (pos = 0; pos < pattlen; pos++)
{
if (patt[pos] != '%' && patt[pos] != '_')
break;
}
for (; pos < pattlen; pos++)
{
/* % and _ are wildcard characters in LIKE */
if (patt[pos] == '%')
{}
else if (patt[pos] == '_')
sel *= ANY_CHAR_SEL;
else
{
if (patt[pos] == '\\')
{
/* Backslash quotes the next character */
pos++;
if (pos >= pattlen)
break;
}
sel *= fixed_char_sel;
fixed_char_sel += (1.0 - fixed_char_sel) * CDB_ROLLOFF_SEL;
}
}
/* CDB: If no trailing wildcard, reduce selectivity slightly. */
if (pos > 0 &&
patt[pos-1] != '%')
sel *= CDB_RANCHOR_SEL;
else if (pos >= 2 &&
patt[pos-2] == '\\')
sel *= CDB_RANCHOR_SEL;
return sel;
}
static Selectivity
regex_selectivity_sub(char *patt, int pattlen, bool case_insensitive)
{
Selectivity sel = 1.0;
Selectivity fixed_char_sel = FIXED_CHAR_SEL;
Selectivity csel;
int paren_depth = 0;
int paren_pos = 0; /* dummy init to keep compiler quiet */
int pos;
for (pos = 0; pos < pattlen; pos++)
{
if (patt[pos] == '(')
{
if (paren_depth == 0)
paren_pos = pos; /* remember start of parenthesized item */
paren_depth++;
}
else if (patt[pos] == ')' && paren_depth > 0)
{
paren_depth--;
if (paren_depth == 0)
sel *= regex_selectivity_sub(patt + (paren_pos + 1),
pos - (paren_pos + 1),
case_insensitive);
}
else if (patt[pos] == '|' && paren_depth == 0)
{
/*
* If unquoted | is present at paren level 0 in pattern, we have
* multiple alternatives; sum their probabilities.
*/
sel += regex_selectivity_sub(patt + (pos + 1),
pattlen - (pos + 1),
case_insensitive);
break; /* rest of pattern is now processed */
}
else if (patt[pos] == '[')
{
bool negclass = false;
if (patt[++pos] == '^')
{
negclass = true;
pos++;
}
if (patt[pos] == ']') /* ']' at start of class is not
* special */
pos++;
while (pos < pattlen && patt[pos] != ']')
pos++;
if (paren_depth == 0)
{
csel = CHAR_RANGE_SEL / FIXED_CHAR_SEL * fixed_char_sel;
sel *= (negclass ? (1.0 - csel) : csel);
fixed_char_sel += (1.0 - fixed_char_sel) * CDB_ROLLOFF_SEL;
}
}
else if (patt[pos] == '.')
{
if (paren_depth == 0)
sel *= Max(ANY_CHAR_SEL, fixed_char_sel);
}
else if (patt[pos] == '*' ||
patt[pos] == '?' ||
patt[pos] == '+')
{}
else if (patt[pos] == '{')
{
while (pos < pattlen && patt[pos] != '}')
pos++;
}
else
{
/* backslash quotes the next character */
if (patt[pos] == '\\')
{
pos++;
if (pos >= pattlen)
break;
}
if (paren_depth == 0)
{
sel *= fixed_char_sel;
fixed_char_sel += (1.0 - fixed_char_sel) * CDB_ROLLOFF_SEL;
}
}
}
if (sel > 1.0)
sel = 1.0;
return sel;
}
static Selectivity
regex_selectivity(Const *patt_const, bool case_insensitive)
{
Selectivity sel;
char *patt;
int pattlen;
Oid typeid = patt_const->consttype;
/*
* Should be unnecessary, there are no bytea regex operators defined. As
* such, it should be noted that the rest of this function has *not* been
* made safe for binary (possibly NULL containing) strings.
*/
if (typeid == BYTEAOID)
ereport(ERROR,
(errcode(ERRCODE_FEATURE_NOT_SUPPORTED),
errmsg("regular-expression matching not supported on type bytea")));
/* the right-hand const is type text for all of these */
patt = DatumGetCString(DirectFunctionCall1(textout, patt_const->constvalue));
pattlen = strlen(patt);
/* If patt doesn't end with $, consider it to have a trailing wildcard */
if (pattlen > 0 && patt[pattlen - 1] == '$' &&
(pattlen == 1 || patt[pattlen - 2] != '\\'))
{
/* has trailing $ */
sel = regex_selectivity_sub(patt, pattlen - 1, case_insensitive);
sel *= CDB_RANCHOR_SEL;
}
else
{
/* no trailing $ */
sel = regex_selectivity_sub(patt, pattlen, case_insensitive);
}
return sel;
}
static Selectivity
pattern_selectivity(Const *patt, Pattern_Type ptype)
{
Selectivity result;
switch (ptype)
{
case Pattern_Type_Like:
result = like_selectivity(patt, false);
break;
case Pattern_Type_Like_IC:
result = like_selectivity(patt, true);
break;
case Pattern_Type_Regex:
result = regex_selectivity(patt, false);
break;
case Pattern_Type_Regex_IC:
result = regex_selectivity(patt, true);
break;
default:
elog(ERROR, "unrecognized ptype: %d", (int) ptype);
result = 1.0; /* keep compiler quiet */
break;
}
return result;
}
/*
* Try to generate a string greater than the given string or any
* string it is a prefix of. If successful, return a palloc'd string
* in the form of a Const node; else return NULL.
*
* The caller must provide the appropriate "less than" comparison function
* for testing the strings.
*
* The key requirement here is that given a prefix string, say "foo",
* we must be able to generate another string "fop" that is greater than
* all strings "foobar" starting with "foo". We can test that we have
* generated a string greater than the prefix string, but in non-C locales
* that is not a bulletproof guarantee that an extension of the string might
* not sort after it; an example is that "foo " is less than "foo!", but it
* is not clear that a "dictionary" sort ordering will consider "foo!" less
* than "foo bar". CAUTION: Therefore, this function should be used only for
* estimation purposes when working in a non-C locale.
*
* To try to catch most cases where an extended string might otherwise sort
* before the result value, we determine which of the strings "Z", "z", "y",
* and "9" is seen as largest by the locale, and append that to the given
* prefix before trying to find a string that compares as larger.
*
* If we max out the righthand byte, truncate off the last character
* and start incrementing the next. For example, if "z" were the last
* character in the sort order, then we could produce "foo" as a
* string greater than "fonz".
*
* This could be rather slow in the worst case, but in most cases we
* won't have to try more than one or two strings before succeeding.
*/
Const *
make_greater_string(const Const *str_const, FmgrInfo *ltproc)
{
Oid datatype = str_const->consttype;
char *workstr;
int len;
Datum cmpstr;
text *cmptxt = NULL;
/*
* Get a modifiable copy of the prefix string in C-string format,
* and set up the string we will compare to as a Datum. In C locale
* this can just be the given prefix string, otherwise we need to add
* a suffix. Types NAME and BYTEA sort bytewise so they don't need
* a suffix either.
*/
if (datatype == NAMEOID)
{
workstr = DatumGetCString(DirectFunctionCall1(nameout,
str_const->constvalue));
len = strlen(workstr);
cmpstr = str_const->constvalue;
}
else if (datatype == BYTEAOID)
{
bytea *bstr = DatumGetByteaP(str_const->constvalue);
len = VARSIZE(bstr) - VARHDRSZ;
workstr = (char *) palloc(len);
memcpy(workstr, VARDATA(bstr), len);
if ((Pointer) bstr != DatumGetPointer(str_const->constvalue))
pfree(bstr);
cmpstr = str_const->constvalue;
}
else
{
workstr = DatumGetCString(DirectFunctionCall1(textout,
str_const->constvalue));
len = strlen(workstr);
if (lc_collate_is_c() || len == 0)
cmpstr = str_const->constvalue;
else
{
/* If first time through, determine the suffix to use */
static char suffixchar = 0;
if (!suffixchar)
{
char *best;
best = "Z";
if (varstr_cmp(best, 1, "z", 1) < 0)
best = "z";
if (varstr_cmp(best, 1, "y", 1) < 0)
best = "y";
if (varstr_cmp(best, 1, "9", 1) < 0)
best = "9";
suffixchar = *best;
}
/* And build the string to compare to */
cmptxt = (text *) palloc(VARHDRSZ + len + 1);
SET_VARSIZE(cmptxt, VARHDRSZ + len + 1);
memcpy(VARDATA(cmptxt), workstr, len);
*(VARDATA(cmptxt) + len) = suffixchar;
cmpstr = PointerGetDatum(cmptxt);
}
}
while (len > 0)
{
unsigned char *lastchar = (unsigned char *) (workstr + len - 1);
unsigned char savelastchar = *lastchar;
/*
* Try to generate a larger string by incrementing the last byte.
*/
while (*lastchar < (unsigned char) 255)
{
Const *workstr_const;
(*lastchar)++;
if (datatype != BYTEAOID)
{
/* do not generate invalid encoding sequences */
if (!pg_verifymbstr(workstr, len, true))
continue;
workstr_const = string_to_const(workstr, datatype);
}
else
workstr_const = string_to_bytea_const(workstr, len);
if (DatumGetBool(FunctionCall2(ltproc,
cmpstr,
workstr_const->constvalue)))
{
/* Successfully made a string larger than cmpstr */
if (cmptxt)
pfree(cmptxt);
pfree(workstr);
return workstr_const;
}
/* No good, release unusable value and try again */
pfree(DatumGetPointer(workstr_const->constvalue));
pfree(workstr_const);
}
/* restore last byte so we don't confuse pg_mbcliplen */
*lastchar = savelastchar;
/*
* Truncate off the last character, which might be more than 1 byte,
* depending on the character encoding.
*/
if (datatype != BYTEAOID && pg_database_encoding_max_length() > 1)
len = pg_mbcliplen(workstr, len, len - 1);
else
len -= 1;
if (datatype != BYTEAOID)
workstr[len] = '\0';
}
/* Failed... */
if (cmptxt)
pfree(cmptxt);
pfree(workstr);
return NULL;
}
/*
* Generate a Datum of the appropriate type from a C string.
* Note that all of the supported types are pass-by-ref, so the
* returned value should be pfree'd if no longer needed.
*/
static Datum
string_to_datum(const char *str, Oid datatype)
{
Assert(str != NULL);
/*
* We cheat a little by assuming that textin() will do for bpchar and
* varchar constants too...
*/
if (datatype == NAMEOID)
return DirectFunctionCall1(namein, CStringGetDatum((char *) str));
else if (datatype == BYTEAOID)
return DirectFunctionCall1(byteain, CStringGetDatum((char *) str));
else
return DirectFunctionCall1(textin, CStringGetDatum((char *) str));
}
/*
* Generate a Const node of the appropriate type from a C string.
*/
static Const *
string_to_const(const char *str, Oid datatype)
{
Datum conval = string_to_datum(str, datatype);
return makeConst(datatype, -1, ((datatype == NAMEOID) ? NAMEDATALEN : -1),
conval, false, false);
}
/*
* Generate a Const node of bytea type from a binary C string and a length.
*/
static Const *
string_to_bytea_const(const char *str, size_t str_len)
{
bytea *bstr = palloc(VARHDRSZ + str_len);
Datum conval;
memcpy(VARDATA(bstr), str, str_len);
SET_VARSIZE(bstr, VARHDRSZ + str_len);
conval = PointerGetDatum(bstr);
return makeConst(BYTEAOID, -1, -1, conval, false, false);
}
/*-------------------------------------------------------------------------
*
* Index cost estimation functions
*
* genericcostestimate is a general-purpose estimator for use when we
* don't have any better idea about how to estimate. Index-type-specific
* knowledge can be incorporated in the type-specific routines.
*
* One bit of index-type-specific knowledge we can relatively easily use
* in genericcostestimate is the estimate of the number of index tuples
* visited. If numIndexTuples is not 0 then it is used as the estimate,
* otherwise we compute a generic estimate.
*
*-------------------------------------------------------------------------
*/
static void
genericcostestimate(PlannerInfo *root,
IndexOptInfo *index, List *indexQuals,
RelOptInfo *outer_rel,
double numIndexTuples,
Cost *indexStartupCost,
Cost *indexTotalCost,
Selectivity *indexSelectivity,
double *indexCorrelation)
{
double numIndexPages;
double num_sa_scans;
double num_outer_scans;
double num_scans;
QualCost index_qual_cost;
double qual_op_cost;
double qual_arg_cost;
List *selectivityQuals;
ListCell *l;
/*
* If the index is partial, AND the index predicate with the explicitly
* given indexquals to produce a more accurate idea of the index
* selectivity. This may produce redundant clauses. We get rid of exact
* duplicates in the code below. We expect that most cases of partial
* redundancy (such as "x < 4" from the qual and "x < 5" from the
* predicate) will be recognized and handled correctly by
* clauselist_selectivity(). This assumption is somewhat fragile, since
* it depends on predicate_implied_by() and clauselist_selectivity()
* having similar capabilities, and there are certainly many cases where
* we will end up with a too-low selectivity estimate. This will bias the
* system in favor of using partial indexes where possible, which is not
* necessarily a bad thing. But it'd be nice to do better someday.
*
* Note that index->indpred and indexQuals are both in implicit-AND form,
* so ANDing them together just takes merging the lists. However,
* eliminating duplicates is a bit trickier because indexQuals contains
* RestrictInfo nodes and the indpred does not. It is okay to pass a
* mixed list to clauselist_selectivity, but we have to work a bit to
* generate a list without logical duplicates. (We could just list_union
* indpred and strippedQuals, but then we'd not get caching of per-qual
* selectivity estimates.)
*/
if (index->indpred != NIL)
{
List *strippedQuals;
List *predExtraQuals;
strippedQuals = get_actual_clauses(indexQuals);
predExtraQuals = list_difference(index->indpred, strippedQuals);
selectivityQuals = list_concat(predExtraQuals, indexQuals);
}
else
selectivityQuals = indexQuals;
/*
* Check for ScalarArrayOpExpr index quals, and estimate the number of
* index scans that will be performed.
*/
num_sa_scans = 1;
foreach(l, indexQuals)
{
RestrictInfo *rinfo = (RestrictInfo *) lfirst(l);
if (IsA(rinfo->clause, ScalarArrayOpExpr))
{
ScalarArrayOpExpr *saop = (ScalarArrayOpExpr *) rinfo->clause;
int alength = estimate_array_length(lsecond(saop->args));
if (alength > 1)
num_sa_scans *= alength;
}
}
/* Estimate the fraction of main-table tuples that will be visited */
*indexSelectivity = clauselist_selectivity(root, selectivityQuals,
index->rel->relid,
JOIN_INNER,
false /* use_damping */);
/*
* If caller didn't give us an estimate, estimate the number of index
* tuples that will be visited. We do it in this rather peculiar-looking
* way in order to get the right answer for partial indexes.
*/
if (numIndexTuples <= 0.0)
{
numIndexTuples = *indexSelectivity * index->rel->tuples;
/*
* The above calculation counts all the tuples visited across all
* scans induced by ScalarArrayOpExpr nodes. We want to consider the
* average per-indexscan number, so adjust. This is a handy place to
* round to integer, too. (If caller supplied tuple estimate, it's
* responsible for handling these considerations.)
*/
numIndexTuples = rint(numIndexTuples / num_sa_scans);
}
/*
* We can bound the number of tuples by the index size in any case. Also,
* always estimate at least one tuple is touched, even when
* indexSelectivity estimate is tiny.
*/
if (numIndexTuples > index->tuples)
numIndexTuples = index->tuples;
if (numIndexTuples < 1.0)
numIndexTuples = 1.0;
/*
* Estimate the number of index pages that will be retrieved.
*
* We use the simplistic method of taking a pro-rata fraction of the total
* number of index pages. In effect, this counts only leaf pages and not
* any overhead such as index metapage or upper tree levels. In practice
* this seems a better approximation than charging for access to the upper
* levels, perhaps because those tend to stay in cache under load.
*/
if (index->pages > 1 && index->tuples > 1)
numIndexPages = ceil(numIndexTuples * index->pages / index->tuples);
else
numIndexPages = 1.0;
/*
* Now compute the disk access costs.
*
* The above calculations are all per-index-scan. However, if we are in a
* nestloop inner scan, we can expect the scan to be repeated (with
* different search keys) for each row of the outer relation. Likewise,
* ScalarArrayOpExpr quals result in multiple index scans. This creates
* the potential for cache effects to reduce the number of disk page
* fetches needed. We want to estimate the average per-scan I/O cost in
* the presence of caching.
*
* We use the Mackert-Lohman formula (see costsize.c for details) to
* estimate the total number of page fetches that occur. While this
* wasn't what it was designed for, it seems a reasonable model anyway.
* Note that we are counting pages not tuples anymore, so we take N = T =
* index size, as if there were one "tuple" per page.
*/
if (outer_rel != NULL && outer_rel->rows > 1)
{
num_outer_scans = outer_rel->rows;
num_scans = num_sa_scans * num_outer_scans;
}
else
{
num_outer_scans = 1;
num_scans = num_sa_scans;
}
if (num_scans > 1)
{
double pages_fetched;
/* total page fetches ignoring cache effects */
pages_fetched = numIndexPages * num_scans;
/* use Mackert and Lohman formula to adjust for cache effects */
pages_fetched = index_pages_fetched(pages_fetched,
index->pages,
(double) index->pages,
root);
/*
* Now compute the total disk access cost, and then report a pro-rated
* share for each outer scan. (Don't pro-rate for ScalarArrayOpExpr,
* since that's internal to the indexscan.)
*/
*indexTotalCost = (pages_fetched * random_page_cost) / num_outer_scans;
}
else
{
/*
* For a single index scan, we just charge random_page_cost per page
* touched.
*/
*indexTotalCost = numIndexPages * random_page_cost;
}
/*
* A difficulty with the leaf-pages-only cost approach is that for small
* selectivities (eg, single index tuple fetched) all indexes will look
* equally attractive because we will estimate exactly 1 leaf page to be
* fetched. All else being equal, we should prefer physically smaller
* indexes over larger ones. (An index might be smaller because it is
* partial or because it contains fewer columns; presumably the other
* columns in the larger index aren't useful to the query, or the larger
* index would have better selectivity.)
*
* We can deal with this by adding a very small "fudge factor" that
* depends on the index size. The fudge factor used here is one
* random_page_cost per 100000 index pages, which should be small enough
* to not alter index-vs-seqscan decisions, but will prevent indexes of
* different sizes from looking exactly equally attractive.
*/
*indexTotalCost += index->pages * random_page_cost / 100000.0;
/*
* CPU cost: any complex expressions in the indexquals will need to be
* evaluated once at the start of the scan to reduce them to runtime keys
* to pass to the index AM (see nodeIndexscan.c). We model the per-tuple
* CPU costs as cpu_index_tuple_cost plus one cpu_operator_cost per
* indexqual operator. Because we have numIndexTuples as a per-scan
* number, we have to multiply by num_sa_scans to get the correct result
* for ScalarArrayOpExpr cases.
*
* Note: this neglects the possible costs of rechecking lossy operators
* and OR-clause expressions. Detecting that that might be needed seems
* more expensive than it's worth, though, considering all the other
* inaccuracies here ...
*/
cost_qual_eval(&index_qual_cost, indexQuals, root);
qual_op_cost = cpu_operator_cost * list_length(indexQuals);
qual_arg_cost = index_qual_cost.startup +
index_qual_cost.per_tuple - qual_op_cost;
if (qual_arg_cost < 0) /* just in case... */
qual_arg_cost = 0;
*indexStartupCost = qual_arg_cost;
*indexTotalCost += qual_arg_cost;
*indexTotalCost += numIndexTuples * num_sa_scans * (cpu_index_tuple_cost + qual_op_cost);
/*
* We also add a CPU-cost component to represent the general costs of
* starting an indexscan, such as analysis of btree index keys and
* initial tree descent. This is estimated at 100x cpu_operator_cost,
* which is a bit arbitrary but seems the right order of magnitude.
* (As noted above, we don't charge any I/O for touching upper tree
* levels, but charging nothing at all has been found too optimistic.)
*
* Although this is startup cost with respect to any one scan, we add
* it to the "total" cost component because it's only very interesting
* in the many-ScalarArrayOpExpr-scan case, and there it will be paid
* over the life of the scan node.
*/
*indexTotalCost += num_sa_scans * 100.0 * cpu_operator_cost;
/*
* Generic assumption about index correlation: there isn't any.
*/
*indexCorrelation = 0.0;
}
Datum
btcostestimate(PG_FUNCTION_ARGS)
{
PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0);
IndexOptInfo *index = (IndexOptInfo *) PG_GETARG_POINTER(1);
List *indexQuals = (List *) PG_GETARG_POINTER(2);
RelOptInfo *outer_rel = (RelOptInfo *) PG_GETARG_POINTER(3);
Cost *indexStartupCost = (Cost *) PG_GETARG_POINTER(4);
Cost *indexTotalCost = (Cost *) PG_GETARG_POINTER(5);
Selectivity *indexSelectivity = (Selectivity *) PG_GETARG_POINTER(6);
double *indexCorrelation = (double *) PG_GETARG_POINTER(7);
Oid relid;
AttrNumber colnum;
HeapTuple tuple;
double numIndexTuples;
List *indexBoundQuals;
int indexcol;
bool eqQualHere;
bool found_saop;
double num_sa_scans;
ListCell *l;
cqContext *stacqCtx;
/*
* CDB: Tell caller how many leading indexcols are matched by '=' quals.
*
* CDB TODO: The num_leading_eq field doesn't really belong in IndexOptInfo.
* It's just a kludgy way to return an extra result parameter, because we
* don't have access here to the IndexPath node where this info should go.
*/
index->num_leading_eq = 0;
/*
* For a btree scan, only leading '=' quals plus inequality quals for the
* immediately next attribute contribute to index selectivity (these are
* the "boundary quals" that determine the starting and stopping points of
* the index scan). Additional quals can suppress visits to the heap, so
* it's OK to count them in indexSelectivity, but they should not count
* for estimating numIndexTuples. So we must examine the given indexQuals
* to find out which ones count as boundary quals. We rely on the
* knowledge that they are given in index column order.
*
* For a RowCompareExpr, we consider only the first column, just as
* rowcomparesel() does.
*
* If there's a ScalarArrayOpExpr in the quals, we'll actually perform N
* index scans not one, but the ScalarArrayOpExpr's operator can be
* considered to act the same as it normally does.
*/
indexBoundQuals = NIL;
indexcol = 0;
eqQualHere = false;
found_saop = false;
num_sa_scans = 1;
foreach(l, indexQuals)
{
RestrictInfo *rinfo = (RestrictInfo *) lfirst(l);
Expr *clause;
Node *leftop,
*rightop;
Oid clause_op;
int op_strategy;
Assert(IsA(rinfo, RestrictInfo));
clause = rinfo->clause;
if (IsA(clause, OpExpr))
{
leftop = get_leftop(clause);
rightop = get_rightop(clause);
clause_op = ((OpExpr *) clause)->opno;
}
else if (IsA(clause, RowCompareExpr))
{
RowCompareExpr *rc = (RowCompareExpr *) clause;
leftop = (Node *) linitial(rc->largs);
rightop = (Node *) linitial(rc->rargs);
clause_op = linitial_oid(rc->opnos);
}
else if (IsA(clause, ScalarArrayOpExpr))
{
ScalarArrayOpExpr *saop = (ScalarArrayOpExpr *) clause;
leftop = (Node *) linitial(saop->args);
rightop = (Node *) lsecond(saop->args);
clause_op = saop->opno;
found_saop = true;
}
else
{
elog(ERROR, "unsupported indexqual type: %d",
(int) nodeTag(clause));
continue; /* keep compiler quiet */
}
if (match_index_to_operand(leftop, indexcol, index))
{
/* clause_op is correct */
}
else if (match_index_to_operand(rightop, indexcol, index))
{
/* Must flip operator to get the opclass member */
clause_op = get_commutator(clause_op);
}
else
{
/* Must be past the end of quals for indexcol, try next */
if (!eqQualHere)
break; /* done if no '=' qual for indexcol */
indexcol++;
eqQualHere = false;
if (match_index_to_operand(leftop, indexcol, index))
{
/* clause_op is correct */
}
else if (match_index_to_operand(rightop, indexcol, index))
{
/* Must flip operator to get the opclass member */
clause_op = get_commutator(clause_op);
}
else
{
/* No quals for new indexcol, so we are done */
break;
}
}
op_strategy = get_op_opclass_strategy(clause_op,
index->classlist[indexcol]);
Assert(op_strategy != 0); /* not a member of opclass?? */
if (op_strategy == BTEqualStrategyNumber)
{
eqQualHere = true;
/* CDB: Count leading indexcols having '=' quals. */
if (!IsA(clause, ScalarArrayOpExpr))
index->num_leading_eq = indexcol + 1;
}
/* count up number of SA scans induced by indexBoundQuals only */
if (IsA(clause, ScalarArrayOpExpr))
{
ScalarArrayOpExpr *saop = (ScalarArrayOpExpr *) clause;
int alength = estimate_array_length(lsecond(saop->args));
if (alength > 1)
num_sa_scans *= alength;
}
indexBoundQuals = lappend(indexBoundQuals, rinfo);
}
/*
* If index is unique and we found an '=' clause for each column, we can
* just assume numIndexTuples = 1 and skip the expensive
* clauselist_selectivity calculations.
*/
if (index->unique &&
index->num_leading_eq == index->ncolumns)
numIndexTuples = 1.0;
else
{
Selectivity btreeSelectivity;
btreeSelectivity = clauselist_selectivity(root, indexBoundQuals,
index->rel->relid,
JOIN_INNER,
false /* use_damping */);
numIndexTuples = btreeSelectivity * index->rel->tuples;
/*
* As in genericcostestimate(), we have to adjust for any
* ScalarArrayOpExpr quals included in indexBoundQuals, and then
* round to integer.
*/
numIndexTuples = rint(numIndexTuples / num_sa_scans);
}
genericcostestimate(root, index, indexQuals, outer_rel, numIndexTuples,
indexStartupCost, indexTotalCost,
indexSelectivity, indexCorrelation);
/*
* If we can get an estimate of the first column's ordering correlation C
* from pg_statistic, estimate the index correlation as C for a
* single-column index, or C * 0.75 for multiple columns. (The idea here
* is that multiple columns dilute the importance of the first column's
* ordering, but don't negate it entirely. Before 8.0 we divided the
* correlation by the number of columns, but that seems too strong.)
*
* We can skip all this if we found a ScalarArrayOpExpr, because then the
* call must be for a bitmap index scan, and the caller isn't going to
* care what the index correlation is.
*/
if (found_saop)
PG_RETURN_VOID();
if (index->indexkeys[0] != 0)
{
/* Simple variable --- look to stats for the underlying table */
relid = getrelid(index->rel->relid, root->parse->rtable);
Assert(relid != InvalidOid);
colnum = index->indexkeys[0];
}
else
{
/* Expression --- maybe there are stats for the index itself */
relid = index->indexoid;
colnum = 1;
}
stacqCtx = caql_beginscan(
NULL,
cql("SELECT * FROM pg_statistic "
" WHERE starelid = :1 "
" AND staattnum = :2 ",
ObjectIdGetDatum(relid),
Int16GetDatum(colnum)));
tuple = caql_getnext(stacqCtx);
if (HeapTupleIsValid(tuple))
{
float4 *numbers;
int nnumbers;
if (get_attstatsslot(tuple, InvalidOid, 0,
STATISTIC_KIND_CORRELATION,
index->ordering[0],
NULL, NULL, &numbers, &nnumbers))
{
double varCorrelation;
Assert(nnumbers == 1);
varCorrelation = numbers[0];
if (index->ncolumns > 1)
*indexCorrelation = varCorrelation * 0.75;
else
*indexCorrelation = varCorrelation;
free_attstatsslot(InvalidOid, NULL, 0, numbers, nnumbers);
}
}
caql_endscan(stacqCtx);
PG_RETURN_VOID();
}
Datum
hashcostestimate(PG_FUNCTION_ARGS)
{
PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0);
IndexOptInfo *index = (IndexOptInfo *) PG_GETARG_POINTER(1);
List *indexQuals = (List *) PG_GETARG_POINTER(2);
RelOptInfo *outer_rel = (RelOptInfo *) PG_GETARG_POINTER(3);
Cost *indexStartupCost = (Cost *) PG_GETARG_POINTER(4);
Cost *indexTotalCost = (Cost *) PG_GETARG_POINTER(5);
Selectivity *indexSelectivity = (Selectivity *) PG_GETARG_POINTER(6);
double *indexCorrelation = (double *) PG_GETARG_POINTER(7);
genericcostestimate(root, index, indexQuals, outer_rel, 0.0,
indexStartupCost, indexTotalCost,
indexSelectivity, indexCorrelation);
PG_RETURN_VOID();
}
Datum
gistcostestimate(PG_FUNCTION_ARGS)
{
PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0);
IndexOptInfo *index = (IndexOptInfo *) PG_GETARG_POINTER(1);
List *indexQuals = (List *) PG_GETARG_POINTER(2);
RelOptInfo *outer_rel = (RelOptInfo *) PG_GETARG_POINTER(3);
Cost *indexStartupCost = (Cost *) PG_GETARG_POINTER(4);
Cost *indexTotalCost = (Cost *) PG_GETARG_POINTER(5);
Selectivity *indexSelectivity = (Selectivity *) PG_GETARG_POINTER(6);
double *indexCorrelation = (double *) PG_GETARG_POINTER(7);
genericcostestimate(root, index, indexQuals, outer_rel, 0.0,
indexStartupCost, indexTotalCost,
indexSelectivity, indexCorrelation);
PG_RETURN_VOID();
}
Datum
gincostestimate(PG_FUNCTION_ARGS)
{
PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0);
IndexOptInfo *index = (IndexOptInfo *) PG_GETARG_POINTER(1);
List *indexQuals = (List *) PG_GETARG_POINTER(2);
RelOptInfo *outer_rel = (RelOptInfo *) PG_GETARG_POINTER(3);
Cost *indexStartupCost = (Cost *) PG_GETARG_POINTER(4);
Cost *indexTotalCost = (Cost *) PG_GETARG_POINTER(5);
Selectivity *indexSelectivity = (Selectivity *) PG_GETARG_POINTER(6);
double *indexCorrelation = (double *) PG_GETARG_POINTER(7);
genericcostestimate(root, index, indexQuals, outer_rel, 0.0,
indexStartupCost, indexTotalCost,
indexSelectivity, indexCorrelation);
PG_RETURN_VOID();
}
Datum
bmcostestimate(PG_FUNCTION_ARGS)
{
PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0);
IndexOptInfo *index = (IndexOptInfo *) PG_GETARG_POINTER(1);
List *indexQuals = (List *) PG_GETARG_POINTER(2);
RelOptInfo *outer_rel = (RelOptInfo *) PG_GETARG_POINTER(3);
Cost *indexStartupCost = (Cost *) PG_GETARG_POINTER(4);
Cost *indexTotalCost = (Cost *) PG_GETARG_POINTER(5);
Selectivity *indexSelectivity = (Selectivity *) PG_GETARG_POINTER(6);
double *indexCorrelation = (double *) PG_GETARG_POINTER(7);
List *selectivityQuals;
double numIndexTuples;
List *groupExprs = NIL;
int i;
double numDistinctValues;
/*
* Estimate the number of index tuples. This is basically the same
* as the one in genericcostestimate(), except that
* (1) We don't consider ScalarArrayOpExpr in the calculation, since
* each value has its own bit vector.
* (2) since the bitmap index stores bit vectors, one for each distinct
* value, we adjust the number of index tuples by dividing the
* value with the number of distinct values.
*/
if (index->indpred != NIL)
{
List *strippedQuals;
List *predExtraQuals;
strippedQuals = get_actual_clauses(indexQuals);
predExtraQuals = list_difference(index->indpred, strippedQuals);
selectivityQuals = list_concat(predExtraQuals, indexQuals);
}
else
selectivityQuals = indexQuals;
/* Estimate the fraction of main-table tuples that will be visited */
*indexSelectivity = clauselist_selectivity(root, selectivityQuals,
index->rel->relid,
JOIN_INNER,
false /* use_damping */);
/*
* Construct a list of index keys, so that we can estimate the number
* of distinct values for those keys.
*/
for (i = 0; i < index->ncolumns; i ++)
{
if (index->indexkeys[i] > 0)
{
Var *var = find_indexkey_var(root, index->rel, (AttrNumber) index->indexkeys[i]);
groupExprs = lappend(groupExprs, var);
}
}
if (index->indexprs != NULL)
groupExprs = list_concat_unique(groupExprs, index->indexprs);
Assert(groupExprs != NULL);
numDistinctValues = estimate_num_groups(root, groupExprs, index->rel->rows);
if (numDistinctValues == 0)
numDistinctValues = 1;
numIndexTuples = *indexSelectivity * index->rel->tuples;
numIndexTuples = rint(numIndexTuples / numDistinctValues);
genericcostestimate(root, index, indexQuals, outer_rel, numIndexTuples,
indexStartupCost, indexTotalCost,
indexSelectivity, indexCorrelation);
PG_RETURN_VOID();
}